Đồ án luận văn tài liệu hướng dẫn sinh viên ngành khoa học máy tính và hệ thống thông tin

Chúng tôi đã viết cuốn sách này với mục đích đáp ứng nhu cầu của những sinh viên đang sắp hoàn thành bằng B.Sc. hoặc ThS. bằng cấp. Tuy nhiên, một số loại khác của độc giả có thể tìm thấy cuốn sách này một người bạn đồng hành có giá trị. Chúng tôi hy vọng rằng một số là khác nhau các loại độc giả có thể được hưởng lợi từ cuốn sách, như được nêu dưới đây

Concepts

Motivation and Purpose of the Book

According to the ACM/IEEE Computing Curricula 2005, the computing field is organized into five major disciplines: computer engineering, computer science, information systems, information technology, and software engineering Although the material covered in this book is applicable to all five disciplines, the content emphasizes examples and applications drawn from computer science and information systems.

Computer science and information systems cover a broad spectrum of topics—from artificial intelligence and CASE tools to database systems, human–computer interaction, information systems assessment, programming languages, operating systems, and web-based information systems These fields are multidisciplinary, drawing on natural sciences (mathematics, logic) and human sciences (psychology, philosophy) The breadth and cross-disciplinary nature of these areas do not simplify project work; instead they present profound challenges and intriguing problems Areas such as social science, psychology, mathematics, and engineering have established guidelines and methods for formulating problems and selecting appropriate research approaches.

Because computer science and information systems span a broad range of topics, students often struggle to formulate a suitable project problem, choose appropriate research methods, and structure a coherent written report Many students also feel uncertain about what to expect from a project, how to complete it within the allotted time, and how to achieve its goals—challenges that are understandable given the limited prior experience with a project as complex and wide in scope as a thesis project These concerns are amplified by a shortage of textbooks and references specifically targeted at students undertaking projects in computer science and information systems Moreover, the project is likely the largest academic undertaking a student will face, perhaps in life, making clear guidance and practical insight particularly valuable.

This book examines the project execution process in computing, clarifying the roles and responsibilities of the student, the supervisor, and the examiner Its aim is to bridge the gap between different research methods and describe how projects are typically carried out in computing disciplines By outlining actionable steps, it provides practical guidance for planning and carrying out your project, from defining objectives and selecting methods to managing work and evaluating outcomes The content emphasizes the collaborative workflow among student, supervisor, and examiner and helps readers navigate the requirements of supervision and assessment Overall, it offers a structured approach to help you plan, manage, and complete a successful computing project.

Purposes of Thesis Projects

A project is defined by careful planning, a clearly defined purpose, and a finite duration with a specific start and finish It is undertaken with limited resources—personnel, money, and equipment—to achieve a particular objective.

You can view the thesis project as serving several (sometimes overlapping) purposes:

● Learning more The project is an opportunity for studying a subject in more depth.

● A stepping stone towards finding and securing a job You may view the project as preparation for working life, by practising your skills and knowledge on real- world problems.

● A stepping stone towards graduate studies You may use the project as preparation for graduate studies, by exploring a research problem and learning about the research process.

Universities typically view your project as serving two additional purposes: educational motivation and research motivation These goals, shared by most projects, emphasise the dual aim of enhancing learning and advancing scholarly inquiry.

The educational portion of the project functions as a test that demonstrates mastery of previously acquired knowledge and skills and their application to a more realistic problem than those typically presented in courses, with four learning goals: (1) to develop critical thinking; (2) to enhance the ability to work independently; (3) to increase understanding of how scientific methods can be used as tools for solving problems; and (4) to develop presentation skills, both oral and written By critical thinking we mean approaching unfamiliar material in a systematic and logical way, using creative and diverse yet disciplined approaches to problem solving, supporting opinions with trustworthy evidence, data, and logical reasoning, and recognizing how a problem fits into a larger context Those comfortable with this way of thinking can often apply these acquired skills in everyday life.

The second objective, the research component, deepens understanding of the subject and makes a real contribution to the field by generating new knowledge To achieve this, the project must incorporate original elements; simply repeating others’ work is wasteful unless the aim is to confirm or challenge previous findings The value of the work comes from discoveries that were unknown before and from disseminating those findings so others can build on them Dissemination is essential to ensure knowledge spreads beyond the project team; without sharing results, even substantial learning fails to advance the field, and the project won’t fulfill its goal of expanding knowledge.

In universities, most research is carried out by faculty members and doctoral students However, there are many valuable reasons for linking research and

Undergraduate teaching introduces students to the fascinating world of science and makes the latest knowledge accessible Wherever appropriate, university programs should incorporate findings from current research to keep the curriculum up to date Integrating research methodology and providing opportunities for students to undertake their own research projects—or participate in larger research initiatives—helps develop independent, critical thinkers We believe thesis projects offer excellent opportunities to close the gap between research and teaching.

Participating in such projects benefits students in many ways: teachers pass on knowledge and research methods while offering exposure to leading-edge research activities; whether your project is off-campus or on-campus, you’ll work more closely with faculty in a collegial, informal setting than in traditional courses; the project provides insights into what research is and how it is performed and offers valuable preparation for doctoral studies by including initial training in research methods.

Actors in the Project

In a project, three main actors collaborate: you (the student), the supervisor, and the examiner The student is the central driver, tackling a well-defined problem in a specific area to advance the project, deepen knowledge, and acquire practical methods for approaching, structuring, and solving complex problems The supervisor serves as an ally and critical advisor, offering guidance to support success while openly pointing out strengths and weaknesses, typically drawing on domain expertise in the project's field; through regular dialogue, the supervisor helps establish direction when exploring new areas The examiner provides independent assessment of the work, evaluating progress and outcomes to ensure standards are met and to supply constructive feedback for future work.

In contrast, the examiner is the reviewer who critically evaluates your work and determines or recommends the grade The examiner isn’t necessarily a domain expert in your exact topic, but typically has a solid understanding of the broader subject area Most importantly, the examiner brings substantial experience that enables them to assess both the content and the methodology of your work.

A positive interaction among the three key roles is essential for the successful completion of a project, with these roles typically filled by three different people to ensure diverse perspectives and clear accountability; however, the supervisor and examiner can sometimes be the same person, though many advantages come from keeping the three roles strictly separate, including stronger checks and balances, reduced bias, and clearer governance throughout the project lifecycle.

Process

Understanding the different project purposes and the actors involved sets the stage, but the essential next step is structuring your work to achieve the goals This book argues for applying a process that guides you through the project's stages while delivering its intended outcomes In the following chapters, the book outlines this process, which involves a series of steps designed to move from initiation through planning and execution toward successful project results.

1 Developing your project proposal (Chap 5)

2 Developing your problem description (Chaps 7 and 8)

4 Presenting and analysing your data (Chap 10)

5 Drawing your conclusions and identifying future work (Chap 11)

6 Presenting and defending your work orally (Chap 12)

7 Preparing the final version of your report (Chap 12)

To keep a project on track, examiners and supervisors evaluate progress at checkpoints, with the number of milestones varying by university When they exist, checkpoints typically occur after steps (1), (2), and (5) The first two checkpoints assess the quality of the project proposal and the problem description, while the third serves as the final quality control before the work is presented and defended A strong project proposal with a clear problem description sets the foundation, making the remaining work easier to execute and helping maintain focus After step (7), a final examination determines the grade based on the examiner's recommendation.

Assessment Criteria

Before you begin a project, familiarize yourself with the criteria and the expected standards set by your university or department The project assessment process described here centers on a defined set of criteria that has proven representative of many departments, ensuring the evaluation aligns with common academic benchmarks.

● Degree to which the work is the student’s own work (as opposed to the supervisor)

● Consistency between different parts of the report

● Degree of insight apparent from the arguments presented to support the decisions made in the project

● Ability to differentiate between others’ thoughts and your own

● Ability to handle references and citations

● Degree of insight apparent from the arguments presented to support claims and conclusions

● Degree of insight apparent from discussion in response to relevant questions Other

● How the student performed as opponent

● Fulfilment of deadlines and other formal requirements

These criteria are discussed in more detail in later chapters, especially in Chap 15.

Reading Guidelines

The book is organized into three parts; Part I (Chapters 1–3) provides a general introduction to projects, explaining their purpose and highlighting the distinct characteristics of projects in academia and industry, while also introducing what science is and detailing the roles of the student, supervisor, and examiner Each chapter in this section is independent and can be read in any order, although it is recommended to read all of Part I before proceeding to Part II.

Part two (Chapters 4–12) offers a detailed, step-by-step guide to executing a project, outlining each phase from start to finish These chapters should be read in sequence because they follow the main stages of the project in chronological order, ensuring readers understand how each step connects to the next and supports successful project outcomes.

Part three, comprising Chapters 13 to 15, provides supplementary chapters that offer practical guidance on how to search for relevant information and how to write a clear, effective report It also includes guidelines for examiners and supervisors on how to examine projects, ensuring consistent evaluation and oversight.

The book is best read sitting in a quiet room with a nice cup of tea!

This book outlines a general framework for carrying out thesis projects, emphasizing three core components as essential: identifying the research question or problem, planning time and resources, and choosing a suitable research method to study the question In this section, we explore how a thesis project fits within broader research practice and methods, and we begin by examining the different areas within computer science and information systems.

The Landscape of CS and IS

Computer science and information systems are defined in many different ways, reflecting their broad scope As Edsger W Dijkstra famously observed, “Computer science is no more about computers than astronomy is about telescopes,” a reminder that the field’s core ideas extend beyond hardware To keep this discussion accessible, we won’t delve into every characterization; instead, we present a single general view of the discipline and illustrate it with concrete problems These examples demonstrate the wide reach of computer science and information systems and what the field can achieve in practice.

Newell and Simon, winners of the 1975 ACM Turing Award, characterized computer science as an empirical discipline in which each new artifact—a program, for instance—can be treated as an experiment whose structure and behavior are subject to study From this technological vantage, the field tackles a range of issues, including theoretical topics such as numerical analysis, data structures, and algorithms; how to store and manipulate data, notably through database systems; the interactions among software components and architectures, including client-server, peer-to-peer, two-tier, and three-tier designs; and the techniques and tools used to develop software, encompassing software engineering, programming languages, and operating systems.

The field of Information Systems (IS), as characterised by Allen S Lee (2001), is concerned with the interaction between social and technological issues In other

M Berndtsson et al (eds.), Planning and Implementing your Computing Project - with Success! © Springer 2008

Computer Science and Information Systems research examines the essential link between human and social factors within organizations and the hardware, software, and data components of information technology The IFIP Working Group 8.2 on information systems defines its scope as generating and disseminating descriptive and normative knowledge about the development and use of information technologies in organizational contexts By information technology, we mean technologies that store, transfer, process, or represent information, and by organizational context, the institutional arrangements in which information is used or created.

Three illustrative research problems centered on a specific IT product—a CASE tool—show that the primary concern of each problem can differ Research problems focused on the human and organizational aspects of CASE tools are naturally information systems (IS) oriented, while those focused on the technical aspects of CASE tools are more CS-oriented Consequently, there are different methodological choices for each case, with IS-oriented studies emphasizing organizational context and user behavior, and CS-oriented studies emphasizing architecture, tooling, and computational techniques, guiding researchers to select methods that fit CASE-tool evaluation in practice.

Three problems frame the discussion of CASE tools (computer-aided software engineering) The first problem adopts a human and organizational perspective on specific types of CASE tools, examining how people, roles, and organizational structures influence tool selection, adoption, and use The second problem highlights both technical and human issues within the CASE tool context, considering usability, training, collaboration, workflow integration, and change management alongside tool performance The third problem rests on a technological basis, addressing core technical aspects of CASE tools—architecture, integration, automation, data models, and standards—and evaluating how these elements affect implementation and long-term viability.

1 What are the critical elements that shape the organisational changes associated with the adoption and use of CASE tools? (Orlikowski, 1993, p 310)

2 What features do software developers want from OO-CASE tools? Related to that question is: how well do current OO-CASE tools meet these needs? (Post and Kagan, 2000, p 384)

3 In this paper the meta-CASE system KOGGE will be described In order to illustrate the KOGGE approach it will be shown how KOGGE was used to implement a CASE tool supporting the object-oriented method BON (Ebert et al.,

What is Research?

The term “research” is semantically overloaded given its use in everyday language

Academic research is a disciplined, systematic inquiry aimed at discovering, revising, and applying facts, theories, and methods within a field Its core objective is to generate and disseminate new knowledge that advances understanding and informs practice In the context of student work, research occurs through coursework and projects, and the goal is to produce results that contribute elements of original scientific knowledge while developing rigorous methods, analytical skills, and the ability to communicate findings clearly to the scholarly community.

Science aims to develop knowledge that was previously unknown in its field, with the outcome of the research process representing an original contribution to humanity Therefore, the main goal of scientific inquiry is to reduce, or even eliminate, uncertainty about what we know These findings are principally disseminated through scientific journals and conferences; by contrast, journalism that collects material for an article may resemble research but is not considered scientific research.

To contrast scientific research with research and development (R&D) activities, which are undertaken within commercial organisations, it is instructive to look at the goals You will see that they are different in terms of motivations and activities In a scientific research project, the primary objective is to learn and understand complex phenomena For example, a research institute will undertake research activities which:

● Establish new knowledge which is made available to the public, often by means of publications in academic journals or conferences

● Are not driven by profit; researchers are therefore relatively free to identify and define their research questions

In a commercial setting, there is usually an expectation that the research activities will be centred on business goals, with the aim of contributing to new products or services, which are expected to generate profit for the organisation For example, an R&D division within an organisation might perform activities such as:

● Undertaking research in areas related to the long term business goals

● Monitoring and observing research findings and trends in technology

● Undertaking pilot-projects to analyse and evaluate new technologies

● Exploring trends in technology for their potential adoption by the organisation (e.g to analyse whether a specific research finding, such as a new software architecture, would be suitable for adoption)

● Building research prototypes and platforms for evaluating technologies, and possibly provide the foundation infrastructure for forthcoming development efforts

● Acting as experts and technology champions within the same organisation

An information systems (IS) development organization pursuing a systems development project aims for a successful system as its primary outcome; conversely, a research institution seeking to investigate a question that involves building a system treats the system as a means to explore the issue rather than as an end in itself Although the term 'research' is used in several ways, this book defines it as a systematic problem‑solving activity conducted with care in the given situation, and the research process is characterized by the researcher’s trustworthiness in both how the research is undertaken and in relation to the phenomenon being studied.

Research questions state what you want to learn, while hypotheses are statements of tentative answers to those questions In practice, many researchers explicitly articulate their ideas about these tentative answers as part of theorising and analysing data These guiding ideas are often called propositions rather than hypotheses, but they serve a similar purpose by linking theory to empirical investigation and shaping the interpretation of results.

12 2 Computer Science and Information Systems Research Project have the same function, and therefore we use the term hypothesis throughout the text, to denote both meanings.

Research projects usually begin with a fundamental question that frames the study and guides the inquiry This central question keeps the investigation focused on the project’s purpose, ensuring all efforts stay aligned with the intended goals.

Research questions are typically broad and open at the outset of a project As the study progresses, these questions become more refined and focused on particular aspects of the problem This iterative refinement allows the project to adapt in response to a deeper understanding of the issue, ensuring the research stays aligned with its objectives.

Research Methods

Once you identify a well-defined research question suitable for a project, the next step is to select a rigorous, systematic method Choosing an appropriate methodology is crucial for guiding the study toward a successful completion In other words, the systematic approach is the core of any credible research effort.

Generally, a method is the means, procedure, or technique used to carry out a process in a logical, orderly, and systematic way In research, the method represents an organized approach to problem-solving that includes (1) data collection, (2) formulating a hypothesis or proposition, (3) testing the hypothesis, (4) interpreting results, and (5) stating conclusions that can be independently evaluated by others, commonly described as the scientific method Part of completing a thesis is training in the use of this scientific method, a framework that can be applied when structuring and solving more complex problems You should understand how and why the steps are carried out, and the nature of the problem itself guides the decision as to which method to use; you choose and apply tools once you have established what you are dealing with (the nature of the problem) and what you want to accomplish (hypothesis or proposition testing).

Within research areas that tackle similar problems—characterized by comparable purposes, contexts, or research questions—certain methods consistently reduce threats to validity This reliability stems from researchers confronting related issues interacting and forming a community of practice where best practices and norms develop and become established over time.

Given the wide range of research methods used across different areas of computer science and information systems, it’s advisable to discuss method selection with your supervisor Their training and experience in research can provide valuable guidance to choose an approach that best meets your project’s goals and aligns with the requirements of your field.

Most research methods share core characteristics: a clearly defined problem to formulate, explicit aims and objectives to guide the work, and a dedicated phase in which the problem is investigated Although this book does not exhaustively cover every scientific method, the related idea of methodology—often referred to as a method in fields such as information systems—describes a comprehensive system of methods, principles, and rules that regulate a discipline The term methodology derives from an ancient Greek word denoting the practice of analyzing different methods, highlighting that a methodology provides a structured framework for selecting, applying, and evaluating research methods.

Researchers, whether investigators or participants, bring a priori conceptions, values and experiences to a study, which can shape how we perceive the research question The primary goal is to achieve trustworthy findings—valid results that hold irrespective of personal biases—and rigorous research methods help safeguard this validity There are several potential threats to validity to consider, and it’s important to be aware of the variety of threats that can occur in the actual application of a chosen research method How these threats are addressed can vary somewhat depending on the method and the project, so thoughtful planning matters Later in this work, we discuss these different kinds of threats to validity in more detail.

Quantitative methods originated in the natural sciences, where the aim is to understand how processes are constructed, built, and operate, often expressed through simple laws or general principles Research is typically driven by hypotheses that are formulated and rigorously tested to seek falsification; if a hypothesis withstands testing, it remains valid until proven otherwise Repeatability and independent verification are vital for reliability, offering multiple opportunities to scrutinize results The goal of quantitative research is to develop models, theories, and hypotheses about natural phenomena, with measurement at the core since it provides the link between observation and the formalization of the model, theory, and hypothesis.

Qualitative methods originate in the social sciences and are used to deepen our understanding of a topic within its specific social context, rather than to provide broad explanations These approaches are typically applied in particular social settings to explore how people experience and interpret their world Over time, the literature has proposed numerous styles and variations of qualitative research, reflecting diverse methods and perspectives within the field.

Qualitative research is typically grounded in fieldwork conducted in a limited number of organizational settings Problems are often studied in unique contexts, with the researcher positioned close to the subject and adopting an insider’s perspective, effectively becoming part of the problem situation As a result, analysis focuses on investigating and interpreting human and organizational aspects in relation to technology.

Conducting this type of research shows that the organizational context itself is not static As people and organizational conditions evolve over time, the preconditions for the study and the way the problem is analyzed also shift As a result, the repeatability of experiments may not be possible, highlighting the importance of interpreting findings within their changing organizational context.

14 2 Computer Science and Information Systems Research Project

Although validity threats share common features across research designs, certain methods and research styles are associated with specific strategies to address these threats Later in this book, we review some of the most likely validity threats encountered in qualitative research.

Through reflection on your own and others' research experiences, you gain heightened sensitivity to potential traps that can affect findings This awareness matters because success depends on how thoroughly these threats are considered and addressed in what you can claim In short, addressing validity is linked to minimizing the limitations of your study When we discuss methods later in this book (Chap 8), we will offer further guidance on validity threats, including how to identify and deal with them.

Linkage Between Research and Thesis Projects

Across scientific research at institutes, development activities in R&D, and product development in industrial and organizational settings, research is defined by a systematic process for solving complex problems We believe thesis projects share this core aspect, even when the outcome is not a conventional scientific contribution Thesis work should place a stronger emphasis on developing the learner’s own capabilities—enabling them to carry out a larger project systematically and independently, apply prior knowledge, and acquire new, in-depth knowledge in the project area In this context, research denotes a structured process for addressing research questions and advancing understanding within the project domain.

Throughout your project, you collaborate with examiners and supervisors who are trained in research methodologies, and their guidance helps you shape how you approach and solve problems The strategies they use to analyze and tackle issues will inspire and influence your thinking as you define the topic of your thesis Whether your problem is scientific in nature or arises from an industrial setting, adopting a systematic process to identify and frame a suitable research problem will benefit you This structured approach lays the groundwork for rigorous problem formulation, effective proposal development, and a clear path to your research goals.

Actors Involved, their Roles and Relationships

This chapter outlines the project roles, clarifies each participant's responsibilities, and explains how these roles inter-relate to drive successful delivery within governance structures The participant roles can be characterized as follows, providing a clear framework for accountability, collaboration, and communication among stakeholders, team members, and sponsors.

● The student, who identifies, approaches and solves a problem

● The supervisor, who guides you in your work

● The examiner, who critically assesses your work

Project participation can vary, with a project sometimes including multiple students, supervisors, or examiners In bachelor’s and master’s thesis work, the norm is one student, one supervisor, and one examiner, though some projects may involve more than one person in a given role or multiple examiners, which is not very common for student theses Introducing additional participants in any role creates extra issues that need to be addressed and discussed separately.

Projects are defined by a distinct start and end, giving them a limited duration in which they can be performed They are allocated resources—people, time, money—and have distinct purposes and defined goals Therefore, executing a project centers on optimizing the use of these resources to deliver the intended results.

The Student

Initiative and commitment drive project progress; without them, work stalls and the project risks coming to a halt Supervisors expect a high level of dedication from students, who should respond positively to advice and guidance Embracing feedback helps students develop greater independence, enabling them to tackle increasingly complex problems.

You should always remember that your supervisor is your best friend when doing a project He or she believes in you Otherwise they would not have agreed

Three actors are involved—the learner, the supervisor, and the project—with the supervisor guiding you by highlighting both strengths and areas for improvement to foster your development Together you and your supervisor work on a project that can expand the body of knowledge and deepen our understanding of a specific problem The ultimate reward for a successful project is a meaningful contribution, but achieving it requires a shared commitment to doing high‑quality work (see Fig 3.1).

3.1.1 The Responsibilities of the Student

● Discuss with your supervisor what kind of guidance you find most useful, and what your possible preferences might be with respect to the working routines

● Plan and discuss with your supervisor the topic of the project and the timetable, including a schedule of meetings where appropriate feedback can be given

● Maintain progress according to the agreed schedule, and continuously report your progress to the supervisor

● Keep systematic records of work completed

● Make sure to submit written material to your supervisor in time to allow for discussion and comments before proceeding to the next stage of the project

● Decide on a date, together with your supervisor, when the project should be finished and the report submitted to the department or university encouragemen t & guidance

Examiner Supervisor co-ordination assessment Output Input

● Discuss with your supervisor (taking into account any input from the supervisor) the preparation of the report, and decide when it is ready for submission

● Write up and submit the report within the time limit, and in accordance with local submission guidelines

Address supervisor feedback promptly by acknowledging criticism, guidance, and suggestions and incorporating them into your work Be prepared to undertake any study the supervisor requires, such as directed reading, additional analysis, or methodological training This may include applying a statistical test to analyze your data to validate your findings By documenting how you implement the guidance and aligning your revisions with the supervisor’s expectations, you can strengthen your research outcomes and progress toward your academic goals.

● Be informed about and respect any regulations and considerations, legal as well as ethical, that are relevant for the project

● Drive the project forward and initiate discussions

● Inform your supervisor of any problems or difficulties, technical as well as non-technical, e.g any personal circumstances which prevent you from working on your project

● Take pride in and responsibility for your work; prioritise and organise your work in such a way that it represents your best efforts.

Your supervisor will point out problems in your writing, but they expect you to proofread and present your best effort When your material is carefully proofread, well-structured, and clearly written, the supervisor can spend less time on presentation details and more time discussing your results and future directions, making the most of their time as a resource for your learning Supervisors are busy with limited time, so delivering polished work ensures efficient use of their input In short, this is your project, and the supervisor should not do the work for you.

Managing multi-student projects requires effective coordination and clear task distribution Even when larger projects share general goals, breaking the work into unique parts and assigning them to individual team members helps maintain accountability and progress In some cases, fairly allocating responsibilities can be challenging, but in such situations all members should share equal responsibility for the project's outcome This approach benefits both students and supervisors, as distinct problems assigned to each participant support clarity, even when the team produces one integrated report When a single report is produced, all contributors are equally responsible for its content and quality.

18 3 Actors Involved, their Roles and Relationships

The Supervisor

A supervisor acts as a guide, helping you navigate both the subject area and the process of scientific thinking They usually bring hands-on project experience in the specific field and are knowledgeable about the methods that are relevant and accepted in that discipline.

Your supervisor guides you in selecting and defining the topic boundaries for your project, clarifying the project scope to keep the study focused and manageable This guidance helps ensure the project is feasible within the given time frame by setting boundaries that keep the work at a reasonable size relative to the allotted timeline By defining clear boundaries and a realistic scope, you improve the likelihood of timely completion and a successful project outcome.

The project should align with a reasonable level of complexity appropriate to the candidate’s academic degree, ensuring the work is challenging yet attainable Additionally, the supervisor will help verify that relevant literature and data sources are readily available in the field, providing a solid evidence base for the research.

During the project, the supervisor monitors progress through regular in-person meetings to discuss developments, with email communication used to complement these meetings for timely updates and clearer ongoing oversight.

One of the most important aspects of a thesis project is gaining training in approaching problems in an independent and systematic way Your supervisor often serves as a valuable source of reflection, stimulating you to critically evaluate your own work as well as the work of others and to refine your methods and arguments throughout the research process.

Here we present the responsibilities of a supervisor as a practical, non-exhaustive set of guidelines for the role: the supervisor should set clear goals and priorities, plan and coordinate work, assign tasks based on skills and workload, and monitor progress to ensure timely, high-quality results; communicate effectively with the team and stakeholders, provide ongoing coaching and feedback, and support development opportunities; enforce safety, quality, and compliance standards, manage resources efficiently, and resolve conflicts or issues promptly; and balance accountability with empowerment, lead by example, foster a positive work environment, and help the organization achieve its objectives through continuous improvement.

● Inform you of the instructions of your particular department or university for carrying out a thesis project

● Inform you of assessment criteria and the expected standard of thesis projects

● Discuss dates when your work should be handed in, presented or discussed

● Provide guidelines for how to report the project

● Discuss with you what is expected in terms of how you should work together

● Give guidance concerning the nature of research, the standard expected, relevant literature and sources in the area, and what research methods are considered good practice in the area

● Inform you of relevant regulations and issues, legal as well as ethical, e.g copyright issues, plagiarism

Begin by reviewing your academic background to uncover gaps in knowledge and competencies that could impact your project Identify specific areas where additional training is needed, including not only topic-specific expertise but also language and writing skills that influence clarity and accuracy Use these insights to craft a targeted professional development plan, prioritizing courses, certifications, or practice that strengthen both domain mastery and communication, so you can enhance research quality and overall project success.

● Help you ensure that your project can be completed, including preparation of a report, within the allocated project time, and advise you accordingly

● Meet you regularly and discuss the progress in the project (how often depends on the type of project, and what phase the project is in)

● Request that you hand in written reports (or other material, as appropriate for the type of project), within an agreed time

● Inform you of any inadequacy with respect to progress or the quality of the work, or in the worst case, of failure to reach an acceptable standard

Writing is a continuous process that runs throughout the project, with intensity increasing as deadlines approach The supervisor should guide your writing and the preparation of the report, including providing feedback on at least one complete draft and on the final version before submission or printing The supervisor is not expected to perform major editing or revise drafts; you remain responsible for the work produced in the project, while the supervisor's primary role is to offer advice and guidance in your pursuit of new knowledge.

Well in advance of completion, your supervisor should ensure you are prepared for the oral examination (the defence) This means you understand the role of the oral examination within the overall assessment, are ready to present your work clearly, and can adequately respond to questions about it To boost your presentation skills and readiness for the oral exam, the supervisor can play a major role by arranging opportunities to present your work, for example at departmental seminars.

After the oral examination, the supervisor should advise and assist you in the preparation of the final manuscript, addressing the implications of any recommendations made by the examiner.

A project with multiple supervisors requires clear communication and disciplined working practices among all parties involved To streamline oversight, appoint one supervisor as the primary supervisor who has overall responsibility for the project’s supervision The supervisory team should agree on the best way to coordinate their efforts, define each person’s role in relation to the project, and specify what each will contribute to guiding the student.

The Examiner

An examiner is the person who assesses your project, either continuously during the process or at the end (summative assessment) There are two typical roles an examiner can take: a quality evaluator, who scrutinizes the work against criteria, and a quality assurer, who ensures the integrity and consistency of the assessment process The examiner’s level of involvement depends on which role they adopt, and explaining these two roles clarifies how each one shapes the evaluation of your project.

Fig 3.2 The examiner as quality evaluator important characteristics of each in order to emphasise the differences Of course, in practice the examiner may combine elements from the two roles.

3.3.1 The Examiner as Quality Evaluator

Evaluation of quality uses a result-oriented approach, where the examiner concentrates on the contribution achieved, the complexity of the problem addressed, the usefulness of the proposed solutions, and how effectively the work is presented In short, the assessment rewards impactful, well-structured work that clearly communicates its value and practical implications.

With this assessment model, the examiner is involved only at the very end of the project, when the work is near completion or finished The evaluation is therefore based on the final report and the oral defence, rather than ongoing feedback or meetings Throughout the project there is no project-related communication between the examiner and the student, nor between the examiner and the supervisor This pattern is typical for graduate studies, including doctoral degrees and, in some cases, master’s degrees.

An advantage of this approach is that examiner objectivity is easier to maintain, since the evaluator has no involvement in earlier project stages The drawback is that the examiner cannot assess criteria related to the candidate’s performance and independence in the process, because they lack insight into how the candidate and supervisor have worked together Figure 3.2 illustrates this arrangement, with the axis numbers representing the weeks in a sample project.

3.3.2 The Examiner as Quality Assuror

An effective alternative to judging success solely by the final outcome is to have the examiner review material produced at multiple project checkpoints, which gives a deeper understanding of progress and shifts some quality assurance responsibilities toward ongoing oversight This approach emphasizes a process‑oriented review, focusing on methods, documentation, and intermediate results rather than a single end‑point evaluation.

Do the work to solve the problem

In addition to evaluating results, the examiner monitors your progress at predefined checkpoints, enabling timely assessment and guidance At these checkpoints, the examiner may provide feedback to you and your supervisor, highlighting the strengths and weaknesses of your work and offering actionable suggestions to address any gaps Figure 3.3 illustrates this process, showing how the examiner interacts with you and your supervisor at each stage.

Early involvement of an examiner provides you and your supervisor with an external perspective on the work, since the examiner is not directly involved in the project Regular feedback from the examiner throughout the process reduces the risk that major issues will only be discovered at the end However, the examiner must be cautious: feedback given during the process can influence the final product, which means the final evaluation is partly shaped by earlier comments This makes it harder for examiners to remain objective when judging the finished work To avoid the examiner effectively becoming the supervisor, feedback during the project should be kept to a minimum This approach still offers valuable insight into the process, helps demonstrate how you have matured and progressed, and allows for an additional process-oriented assessment criterion that is not possible when the examiner acts only as a quality evaluator.

At certain points in a project, the examiner may take a more active role in monitoring the process, and to help maximize the number of successful projects, may offer guidance during the early stages In this phase, the examiner can exert a positive influence by providing feedback and support to both the student and supervisor as they define the problem area and establish the project goals.

Another scenario in which the examiner may take a more active role is when a problem arises If you’re unhappy with the supervision or your supervisor is unhappy with your progress, contact the examiner and ask for their advice.

Do the work to solve the problem

Fig 3.3 The examiner as quality assuror

3.3.3 The Responsibilities of an Examiner

An examiner's role is to thoroughly scrutinize your work, pinpointing its strengths and weaknesses, deciding whether you pass or fail, and assigning your grade They will typically initiate a discussion to test your ability to reason about the problem and its solutions from alternative perspectives Common test criteria used in evaluation include clarity of argument, logical coherence, accuracy of content, depth of analysis, the ability to apply concepts to new situations, and the quality of supporting evidence or justification.

● Level of creativity in the process

● Ability to analyse and reason in different situations

● Oral presentation skills and ability to defend the work, i.e to respond to scruti- nising questions

● The relevance and originality of the problem and topic

● How well you are able to separate your own work from the work of others, and how well you are able to manage citations of other work

● How well the project has been managed with respect to time and the project plan

This concludes the first part of the book In the next part, the process is described in detail.

This chapter outlines the thesis project process, detailing seven activities performed by the student, while the supervisor and examiner participate in four activities designed to ensure quality control at different stages Figure 4.1 provides an overview of the process and its stages, illustrating how the student drives the work and how the supervisor and examiner monitor progress to maintain standards throughout the project.

The process starts with an activity in which you develop a project proposal The project proposal is a short description of your initial ideas about what you would like to do, and how you intend to achieve the overall goal of the project The project proposal is submitted for quality control.

Once the project proposal has been accepted, the ideas in your proposal are developed into a more extensive problem description Typically, developing a problem description includes activities such as searching for information at the university library, developing the aim (the overall goal) and objectives (how to reach the aim) of the project, and developing arguments which support the aim The problem description is then submitted for quality control It is a common practice to present the problem description at a seminar where, possibly among others, both your examiner and supervisor are present.

Continue the project workflow by executing the core tasks: following the objectives, collecting and analyzing data, presenting findings, and drawing conclusions from the results As the project nears completion, submit a complete first draft of your report for quality control, a checkpoint that allows your supervisor and examiner to assess progress before you present and defend your work at a seminar.

During the thesis defense and presentation, you receive valuable feedback that helps you refine your arguments, tighten your methodology, and improve the overall clarity of the final thesis This feedback is instrumental for preparing the final version of your report, ensuring it meets academic standards and communicates your research effectively In the concluding stage, your examiner—often with input from your supervisor—evaluates the submitted thesis and assigns a grade based on the quality of research, analysis, and presentation.

The process and activities depicted in Fig 4.1 are explained in detail in the forthcoming chapters.

Fig 4.1 An overview of the process

Choosing a Subject Area

Your subject area defines the topic of your project In computer science and information systems, common subject areas include electronic commerce, software engineering, and human–computer interaction Beyond choosing a subject area, you should describe your project topic in more detail by outlining the specific problem or question you will address, the goals and scope of the work, and the approach you plan to take.

● Database systems Object-oriented databases, relational databases, active databases, multimedia databases, distributed databases, etc

● Electronic commerce Infrastructure, web auctions, web shops, company strategies for implementing electronic commerce, etc

● Software engineering Software testing, object-oriented modelling, CASE tools, rapid prototyping, etc

● Human-computer interaction Usability, interface design, visualisation, etc

These examples show that the names of subject areas often correspond to course names, titles of textbooks, or keywords in research articles.

Interdisciplinary subject areas arise when one field blends with another, such as databases and human–computer interaction, which can merge into user interfaces for database systems Similarly, topics that combine computer science or information science with another discipline—bioinformatics, for example—bring together computing and biology Although these interdisciplinary areas are valuable to explore, they can pose challenges in finding an appropriate supervisor Ideally, a supervisor should have deep knowledge of the related field; in some cases, you may even need two supervisors to cover the different domains effectively.

Choosing a project’s subject area should begin well before the project starts Start exploring potential topics early and refine your chosen area incrementally to ensure a well-motivated decision This deliberate, proactive approach helps prevent rushed, poorly justified choices and lays a solid foundation for the project’s success.

Starting early lets you identify and verify key information sources before the project begins The time required to locate and order relevant literature or to discuss a proposal with a partner company should not be underestimated, and it’s better to handle these steps before the project kicks off rather than once it’s underway You don’t need a full literature analysis upfront, but you should familiarize yourself with the most important sources and confirm their availability when the project starts.

Keep in mind that there are many different sources of information, not only books Libraries usually have several ways of supplying information The first and obvious

Write and submit a project proposal

Focus on and choose a problem within the subject

Assure quality of initial ideas

5.1 Choosing a Subject Area 29 are the library’s own literature resources (books, journals etc.), but most libraries will also organise inter-library loans if they do not have the literature you want.

Most libraries grant access to bibliographies and bibliographic databases—extensive collections of documents searchable by author, title, subject, and keywords—making it easy to discover articles from journals and conferences, including research on new subjects not yet published in books Many libraries also provide full-text databases that deliver complete article texts (often with figures and tables) immediately, offering a fast and effective way to obtain information Examples of useful bibliographies are provided in the appendix.

Beyond your library, consider searching the Internet for additional information on your topic Finding information online can be challenging if you don’t have solid starting points, so try popular search engines to begin your research Be prepared for a flood of results at first, which can make it hard to identify the most relevant information.

Find out whether your university subscribes to Internet access for journals in your field, because this subscription can let you search those journals’ archives for past articles and print any that are useful to your research or coursework.

If you cannot find any literature at the library associated with your subject area, it may be an indication of one of the following:

● Your chosen subject area is too novel for a B.Sc or M.Sc project Your chosen area is more suitable for a Ph.D project.

If your current search isn’t yielding the results you need, consider looking in different places—explore alternative sources such as journals and conference proceedings It often helps to adjust your search strategy by using different keywords or keyword combinations when querying bibliographic databases to uncover relevant literature.

● You are in the wrong library!

Searching for information is discussed in detail in Chap 13.

5.1.2 How to Choose a Subject Area

Which subject area to choose is a decision that only you can make; nobody else will make this decision for you.

Motivation is a key driver of project success, and choosing a subject area that genuinely interests you boosts engagement and sustained effort throughout the work By focusing on the topic you are most passionate about, you set a positive tone for the entire project and improve its quality and outcomes.

Choose a subject area where you have the necessary skills Do not choose a subject area in which, for example, you have failed courses.

Combining two areas usually yields interesting problems and strong topics for project proposals, offering a productive interdisciplinary angle However, avoid projects that span more than two areas, since that extra breadth can introduce complexity that’s hard to manage.

In addition to the above guidelines, ask yourself whether you:

● Have previously in your studies encountered subject areas or courses that you felt were especially interesting

● Would like to work within a particular subject area in the future

Choose Problem to Focus on Within the Subject Area

After choosing a subject area for your project, narrow your focus by identifying a specific problem within that area to explore This concentrates your research on a well-defined objective and guides your inquiry For example, in database systems, a common challenge is mapping a logical database design to a physical database design.

You should try to find problems which are of general interest, or which can be generalised or applied, for example, to several companies or organisations.

Here are some ways to identify a problem within the subject area:

● Ask yourself what you would like to do within a particular area (or what you can do, given your current knowledge).

Begin with a thorough literature review, since other researchers may have already identified the issues worth exploring and may have even completed work similar to what you planned If you discover overlap with existing studies, adjust your objective to target gaps that current sources do not cover Finding this out early helps you avoid reinventing the wheel and guides you toward a unique, valuable contribution.

Reach out to potential supervisors, as they often have ideas about what’s worthwhile and interesting to explore within the subject area Some supervisors may already have written project proposals, typically aligned with their own professional research interests Proposals that connect to a supervisor’s research areas are more likely to motivate them to act as the project supervisor.

Engage with businesses and organizations, because they frequently encounter problems they don’t have the time, expertise, or resources to investigate on their own Such problems or ideas tend to be highly specific, so you should discuss them with a potential supervisor in your department who can help place the company’s particular challenge into a broader context.

Begin your project in an area where you already have the necessary background If you’re attracted to database design after reading an article and decide to tackle a project in that field without prior study of database systems, you’ll spend valuable time learning basic concepts instead of advancing the work This slow start means most early effort goes toward grasping fundamentals rather than making real progress.

5.2 Choose Problem to Focus on Within the Subject Area 31 area It is also much more difficult to identify relevant problems if you are unfamiliar with the area in general.

After identifying a problem, assess whether it is worth exploring further by articulating the research value and potential impact Document clear arguments for why investigating the issue matters, and consult the existing literature to see if the problem remains unsolved; finding such clues suggests you’re on the right track If the literature offers little or no support, you’ll need to develop the justification from first principles Use your initial ideas as a starting point and ask what kind of project you would genuinely enjoy pursuing, and what form it should take—for example, a feasibility study, a data-driven analysis, or an experimental investigation—and then refine your plan accordingly.

● A comparison of theory and practice

Keep in mind that most thesis projects use elements from more than one of the above categories Use the categories to identify the main characteristic of your approach

In the subsequent sections, we take a closer look at each of these project types.

Descriptive projects present the state-of-the art for a given subject A descriptive project can be set up in different ways Here we outline two common types of descriptive projects.

In the first type, the aim is to categorise and compare previous work within a subject area This may include objectives such as (1) categorising previous work,

A systematic survey includes steps such as (2) selecting comparison criteria and (3) comparing previous work with respect to those criteria This type of literature review helps reveal how a subject area has evolved over time, identify its current status, and forecast potential developments in the near future.

In the second type of project, the goal is to understand the current status of the subject and to identify the key factors shaping it This approach typically includes objectives such as selecting relevant questions, conducting interviews with stakeholders, and extracting the most important factors from those conversations By pairing a status assessment with targeted data collection, this method yields actionable insights for decision-makers and provides a solid foundation for informed planning.

In a descriptive research project, avoid producing a report that merely summarizes the literature you have reviewed Instead, deliver a focused literature analysis: synthesize core findings, compare theoretical approaches, critique methodologies, and highlight where evidence converges or diverges This approach shows how the literature informs your research questions and design, emphasizing your synthesis and critical interpretation rather than simple description.

Theory oriented projects often deal with extending or comparing existing theoretical models without testing them in practice Here we outline two common types of theoretical projects.

In this theoretical project, the goal is to extend an existing theory or model by augmenting the relational data model to support business rules Key objectives include identifying the extension details, such as which types of business rules are supported, introducing the extension to the original theoretical framework, and comparing the original model with the extended version to evaluate the added capabilities and trade-offs.

In the second project example, the goal is to compare how well two distinct data models support business rules This comparative analysis of two theoretical models highlights objectives such as selecting appropriate comparison criteria and evaluating each data model against those criteria to determine their effectiveness in implementing, enforcing, and adapting business rules.

Opting for a theoretical project involves understanding how theoretical ideas can be applied in real-world contexts Although the project doesn't involve implementing or testing the theory, the correctness of the theory or model is key to producing credible conclusions.

Applied projects often deal with conducting experiments and building proof-of- principle implementations, and gathering experiences from them Here we outline one common type of applied project.

This applied project aims to gain hands-on experience implementing a web data caching algorithm Key objectives include building a simulator to model cache behavior, implementing the new caching algorithm, conducting thorough tests and analyses of the results, and proposing improvements to enhance performance, efficiency, and overall effectiveness of the caching system.

An applied project should not be a mere consulting assignment; while it may satisfy a company’s immediate goals, it typically falls short of the rigor required for a thesis These efforts can drift away from the connecting theory, reducing their academic impact A robust approach is to frame a practical problem from an industry partner within a coherent theoretical context, which clarifies the research significance and shows why investigating the company’s problem matters.

5.2.4 A Comparison of Theory and Practice

Projects which combine theory and practice may contrast the theory with current practice in companies or organisations Here we outline one example project.

This project contrasts current theory of object-oriented modelling with how organisations actually apply object-oriented modelling in practice Objectives include selecting representative companies or organisations, establishing relevant comparison criteria, examining the theoretical foundations against these criteria, investigating how organisations implement object-oriented modelling relative to the criteria, and comparing the results of the theoretical analysis with observed practice The study highlights gaps between object-oriented modelling concepts and real-world deployment, generating insights for researchers and practitioners to bridge theory and practice in object-oriented modelling.

Assure Quality of Initial Ideas

If you haven't yet discussed your initial project ideas with a prospective supervisor, now is the moment to do so A prospective supervisor can review your ideas for merit, feasibility, and overall quality, providing early feedback that helps you refine your project plan.

Even if a supervisor suggests a project idea, you should write your own project proposal instead of simply copying their description; a strong proposal shows you understand the problem, think independently, and can develop the idea further on your own By reframing the supervisor’s concept in your own words, you demonstrate your grasp of the issue and outline a clear path for development, including your approach to methods, scope, and potential outcomes The proposal should connect the supervisor’s idea to your personal contribution, present a feasible plan, and showcase independent thinking and initiative that extend the original concept rather than echoing it.

Write and Submit a Project Proposal

A project proposal is typically one to a few pages, but the required length and level of detail can vary by department Therefore, check your department's guidelines and standards to understand what is expected Writing should begin as soon as you have chosen the subject area.

After you finish the proposal, submit it for quality control and write with maximum clarity, even if you haven’t read every detail of the problem domain The reviewer—whether a supervisor or examiner—will view the proposal as arising from you, so clear presentation matters When tough questions about the project arise, you must be able to answer them, because at this stage the project is yours, not your supervisor’s.

In this section we present a simple structure for the project proposal (see Fig 5.2) First, you need to introduce your chosen subject area to the reader Second, focus

5.4 Write and Submit a Project Proposal 33 your interest within the subject area on a specific problem Having done this, begin developing arguments that back up your aim and objectives Remember that one of the main purposes of the project proposal is to convince the reader that your project is worthwhile If you can present good arguments for why it is important for someone to undertake your project, as captured by your stated aim, then you have laid the first brick in planning a solid project Do not use subjective arguments such as

A project proposal should be justified by objective reasons tied to gaps in current knowledge, not by the author's personal interest in the topic; otherwise a reviewer could reject it simply by saying "I don’t think X is interesting." The proposal must articulate why the work needs to be done, explicitly identifying unanswered questions, contradictions, or missing data in the existing literature, and explaining how addressing these gaps will advance the field Effective proposals link the significance of the problem to measurable outcomes, potential impact on theory or practice, and feasibility within available resources, and they typically include a succinct literature review that maps where the knowledge gaps lie By framing the project around knowledge gaps and demonstrable value, reviewers can assess the necessity and potential contribution of the research.

The project aim is a question or a problem definition within the subject area that you would like to pursue For example,

To investigate the usage of electronic commerce in small and medium sized companies during the last 10 years.

To ensure originality, the proposal should explicitly identify a missing piece of knowledge not covered by Sections 1.2 and 5.2 and explain how the project will supply it, for example by developing techniques to optimise queries in distributed main-memory databases, which would translate into faster responses for users The aims are achieved through concrete objectives, typically presented as a sequence of activities to carry out, and the proposal should spell out these objectives and map them to project phases, drawing on examples from Sections 5.2.1–5.2.4 to illustrate how the work will be organized and executed.

If it is possible for you to make your preferences known with regard to the choice of supervisor, here are some guidelines for making the right choice:

- To the subject area (e.g., XML documents).

- To the problem within the subject area (e.g., preserving links when transforming XML documents to another data format).

Reasons why it is important to investigate the chosen problem.

A short description of what you intend to do.

How (by what steps) do you intend to achieve the aim of the project?

Fig 5.2 An example structure of a project proposal

● Ask previous students how they were supervised by the person you have in mind.

● Browse theses of students who were supervised in previous years by your potential supervisor.

Verify your potential supervisor's availability before starting your project: ensure they will be present for the majority of the project duration and not away for extended periods, and avoid supervisors who are too heavily involved in other activities.

To select the right supervisor when two candidates are equally strong, assess how active each one is within their research areas A supervisor who shows higher activity in their field can be advantageous for your project While gauging activity can be challenging, a practical approach is to review the descriptions of research activities on their personal web pages.

● Talk to the potential supervisor If you have not met before, it might be good to talk to him or her to see whether you have any personal differences.

If you submit a well-written project proposal to the reviewer, you can get a head start of your project and probably also avoid time-consuming resubmissions of your proposal.

Before submitting the project proposal to the reviewer, check the following:

● Proper language Is the wording in the project proposal clear and concise?

● Mandatory information Does the project proposal contain the required information?

● Quality assurance Have you discussed the project proposal with a potential supervisor or someone else who has knowledge in the chosen subject area?

● Skills and resources Do you have the necessary background and resources to do a project in the chosen subject area?

● Time Have you estimated the time it takes to complete the project? Preferably, your estimation should also include some slack to cater for any project delays.

After you have submitted the project proposal, continue to read the literature, arrange meetings etc while you are waiting for a response from the reviewer.

Quality Control of Project Proposal

Reviewers quickly gauge project proposal quality by checking the clarity of language and the presence of mandatory information, and they assess whether the proposed project’s scope is appropriate They look for proposals that are not too simple, too advanced, too small, too fuzzy, too broad, too specific, or too big, since misaligned scope can undermine feasibility and impact A strong proposal presents clear objectives, deliverables, timelines, and requirements, ensuring it aligns with evaluation criteria and can be realistically implemented.

5.5 Quality Control of Project Proposal 35 these categories are in most cases a sign that you have not carried out enough quality assurance checks before submitting it These problems can be resolved in most cases by further discussions with (potential) supervisors and revision of the proposal.

Matching Supervisors and Students

In this section, we assume the person responsible for matching supervisors and students is already in dialogue with the supervisors The task of pairing supervisors with students is not as simple as it may seem and is often constrained by a number of factors that shape the outcome—most notably alignment of research interests and expertise, supervisory capacity and workload, availability and scheduling, funding or project timelines, and applicable institutional policies Recognizing these constraints helps explain why matches must be carefully negotiated and potentially revised to reach an effective pairing.

Availability of supervisors within a subject area is typically limited in each department, which creates problems when many students want a particular supervisor who possesses unique subject expertise When demand is high, the supervisor’s other commitments may limit supervision to only a couple of students at any one time, leaving the majority without their preferred mentor For example, if ten students all seek supervision from one person, that supervisor may realistically be able to supervise only two due to time and workload constraints, so only two of the ten students will get their preferred supervisor.

Students may hesitate to be supervised by someone only vaguely familiar with their subject, but such supervisors often bring strong project-management experience and excel at asking the right questions to move the work forward When feasible, bringing in an external domain expert as a co-advisor can compensate for the main supervisor’s lack of subject-matter expertise Consequently, a supervisor with only a partial match to the topic does not have to be a disadvantage, since their guidance and probing questions can drive progress, especially with a co-advisor who covers the subject area.

Personal suitability of supervisors is a key factor in the student–supervisor relationship When a supervisor has a personal connection with the student—such as being a close friend—the boundary between friendship and professional supervision can blur, risking biased guidance and biased outcomes Additionally, personal differences or incompatibilities between the supervisor and student may render the supervisor unsuitable for the mentoring task, underscoring the importance of recognizing potential conflicts of interest and maintaining clear professional boundaries throughout the supervision process.

Practical constraints make it impossible to fulfill every student’s request for a preferred supervisor in the project supervision process While some students will be assigned their top choice, others may be paired with a supervisor not on their selection list In most cases this still works, but the person responsible for assigning project supervisors should actively monitor the student–supervisor pairing to identify early signs of incompatibility and take corrective steps to ensure effective project supervision.

When developing the project proposal, you should refresh your knowledge of how to identify appropriate literature and how to use citations.

Appropriate References

A reference is a concise description that identifies an information source and lets readers locate the original work It includes essential details such as the author, year of publication, title, and publication venue in a standardized format For example, a fictional journal article by K Anderson could be cited as: Anderson, K (2008) The Untold Story of Computer Science International Journal of Computer Science, 2(1), 23–35.

By properly referencing the material your work is based upon, you achieve several things You:

● Show how your work extends the current state-of-the-art knowledge in the area

● Show the originality of your work

● Give credit to other people’s work (and thereby avoid being accused of plagiarism)

To produce a credible, evidence-based report, every argument must be supported and validated: each claim should be backed by your own rigorous research or by results published by others, with clear citations to credible sources to strengthen the evidence base and improve transparency.

● Show that you are familiar with the work done in the area

Your reference list serves as a quick indicator of credibility: experienced readers can tell at a glance whether you rely on well-known, credible sources or on less authoritative ones To strengthen your arguments, base your claims on references from high-quality, peer‑reviewed journals or proceedings of renowned conferences, which signals scholarly rigor and enhances the impact and trustworthiness of your report.

Another reason why references are needed is to make your work reproducible Anyone who reads your report should be able to reproduce your work, and therefore

1 Details concerning the information (e.g., volume number, page number) contained in the reference are given in Chapter 14.6.

M Berndtsson et al (eds.), Planning and Implementing your Computing Project - with Success! © Springer 2008 all sources you have used must be clearly identified This is especially important if your work is an extension of an existing solution, method or theory If the reader cannot find the literature containing the original work, then they cannot verify that your extension is based on a correct interpretation of the original solution, method or theory.

Whether a reference is appropriate for your work depends on its content and type You can assess relevance by testing what happens if you remove the reference from the text; if removing it causes you to lose essential information or the argument to fall apart, keep the citation, but if not, it can be removed In practice, choose references that genuinely support or clarify your points and omit those that do not add value.

There are different types of documents that you can use as references:

Scientific research journals typically publish high-quality articles written by experts for other experts, which can make them difficult for non-experts to understand Often, one or more conference or workshop papers precede the journal article, so even a strong journal report may reflect findings that are somewhat outdated Consequently, it is wise to check when the article was first submitted to assess its current relevance.

Conference and workshop proceedings can publish high-quality papers relatively quickly compared with most journals When possible, check the acceptance rate—the number of accepted papers divided by the number of submissions A low acceptance rate (roughly 15–30%) usually indicates stronger research, while a high acceptance rate (80–85%) can raise questions about quality Identify conferences and workshops in your field with solid reputations, and lean on your supervisor for guidance on which venues are credible and well-regarded.

Theses at the master's (M.Sc.) and doctoral (Ph.D.) levels contain valuable information and, to varying degrees, reflect the state of the art in their field Through rigorous examination and assessment, theses are typically of adequate to high quality A Ph.D thesis presents research results that have been developed over three years or more, whereas an M.Sc thesis is usually the product of roughly six months of dedicated research; as a result, doctoral work tends to offer more substantial and mature contributions, while M.Sc theses provide focused, concise insights into a topic.

D thesis is of higher quality and more extensive than similar material in a M.Sc thesis.

● Textbooks are usually a good source for understanding the fundamentals in a variety of subjects and areas These books are frequently used in courses In general, textbooks are reviewed but they do not cover the latest research findings, since they are mainly used for teaching established knowledge New findings take some years to become established, and are therefore not included in most textbooks until some years have passed However, there are also some textbooks that cover advanced issues and the latest research findings These advanced textbooks are typically aimed at researchers and Ph.D students.

Magazines can be viewed as a popular, accessible version of research journals Some magazines publish peer‑reviewed articles, while many do not, offering content that is broader and more reader‑friendly Even when magazines include articles about research results, the material is usually presented in summaries, simplified explanations, or feature stories rather than full original studies As a result, magazines are a valuable resource for quick insights and overviews, but readers should rely on primary research sources and peer‑reviewed journals for in‑depth methods, data, and validation.

6.1 Appropriate References 39 of summaries and often with simplifications, to make the material more accessible to non-experts If such an article contains a reference to a an article in a reseach journal, then you should always try to find the original source When reading magazines that are published by companies that, e.g sell a particular computer program, you should be aware that articles in these magazines will probably not criticise products or statements of the company.

Web pages can contain useful information, but they should generally be avoided as scholarly sources because their content is often not reviewed and they can disappear or change quickly If the same research appears in a conference paper or journal, obtain that article and cite the original source instead of a web URL Use the primary publication for references to ensure accuracy, credibility, and long-term availability.

Newspaper articles can provide useful illustrative examples for a report, but avoid basing your work heavily on newspaper references since they are not usually peer-reviewed and often omit the detailed information your project requires For instance, a piece describing a robot that can play soccer may outline capabilities at a high level but won’t present the algorithms or the software architecture behind the system Those technical details are typically published in research journals or in the proceedings of research conferences If you do cite newspaper sources, look for names of researchers or institutions, as these identifiers help you locate the corresponding research publications.

Other documents, such as manuals, modelling documents, and commercial information, should be treated with care because they are generally not reviewed, and there is no guarantee of quality Moreover, documents published by companies may be written to advertise the company’s products rather than to present neutral facts.

Written documents are generally more reliable than oral statements because you can go back and verify the exact details, while memory can degrade and oral accounts may omit or misremember information In addition, printed material typically undergoes a formal review process before publication, which enhances accuracy and quality and offers a verifiable record.

Citations

A citation is the use of a reference in the text For example, the two following citations highlight that the journal article by K Anderson describes four areas for future research.

Anderson (2008) describes four areas for future research …

There are four main areas for future research (Anderson, 2008).

Using citations and references is essential to clearly distinguish your own work from the ideas and data of others, ensuring proper attribution and academic integrity When you write, cite sources for any ideas, facts, or analyses that are not your own, and use a consistent citation style so readers can verify the origins of those claims This practice strengthens credibility, prevents plagiarism, and makes your report more trustworthy and discoverable by signaling credible sources and clear attribution.

Everything in your report that does not come with a citation will be assumed to be your own work.

Simply put, any factual claim in your report is assumed to be your own discovery unless you include a citation that points to the source where you found it To preserve credibility and avoid misrepresentation, always attribute ideas and data to their original references with in-text citations and a complete reference list Without proper sourcing, your supervisor may conclude that you invented the fact, which can damage trust and the integrity of your work.

Authors must justify and verify statements that lack citations and clearly indicate when material is not their own; while not every sentence needs a citation, neglecting references can jeopardize the project and undermine credibility In examinations or reports you will be asked to defend and justify assertions without an associated source, a task that becomes difficult if you do not know the details of the underlying argument, and the examiner or supervisor may recognize the statement in its original context, risking misattribution if you present it as your own Clear attribution, proper references, and bibliographic integrity strengthen your argument, improve transparency, and align with SEO best practices by using relevant keywords such as citations, references, and plagiarism prevention to enhance search visibility and trust.

Citations in the text follow several rules According to The Chicago Manual of Style (1993, pp 644–645) a text citation can be placed

The human brain contains approximately 50 billion neurons (Smith, 1994).

● At a logical place in a sentence:

According to some researchers (Smith, 1994) there are 50 billion neurons in the human brain.

● At a grammatically correct place in a sentence:

According to Smith (1994), there are 50 billion neurons in the human brain.

In addition, we recommend that the citation be placed

There are five categories of users (Anderson, 2008): (1) students, (2) teachers,

(3) professors, (4) technical staff, (5) administrative staff.

When the reference is placed just before the enumerated list, it becomes very clear that the list is taken from something Anderson published in 2008.

Neuroscience indicates that the human brain houses about 50 billion neurons, with numerous neuronal subtypes still awaiting complete classification This finding underscores the need for innovative tools and techniques to advance neuron categorization and invites researchers to contribute to the ongoing effort to map neuronal diversity.

A common mistake is to place the citation after the last sentence of a paragraph For example:

Computers are indispensable in industry, powering essential services such as booking train tickets However, their growing presence in everyday life is disputed, since widespread computer use can create security problems as people struggle to defend against hackers and Internet viruses Researchers continue to debate these issues (Jones, 1993).

Citation placement at the end of a paragraph can be read as applying to the entire paragraph, a pattern that often confuses readers in academic writing For example, if a page has five paragraphs but only one citation placed after the last paragraph, readers aren’t sure whether the citation supports all five paragraphs or just the final one The core issue is that citations lack an inherent scope; simply where a citation appears does not define how much text it relates to To avoid ambiguity and improve reader comprehension, writers should align citation placement with precise wording—using inline citations or multiple citations when needed—so readers can correctly infer what is being supported and maintain paragraph cohesion.

Here is a further example of how citations may be made in the text, and how they can be improved:

For a long time, the best stock market predictions have been achieved by the Epsilon neural network architecture (Myers and Sang, 1997, Niven, 1999).

In this case, Myers and Sang could be either the developers of the Epsilon architecture or researchers who have systematically evaluated all stock-market prediction programs To avoid ambiguity, a clearer citation would specify their exact role, for example: “Myers and Sang, developers of the Epsilon architecture,” or “Myers and Sang, researchers who conducted a comprehensive evaluation of stock-market prediction programs.”

The Epsilon neural network architecture, proposed by Myers and Sang (1997), has for a long time been the most accurate method for stock market prediction (Niven, 1999).

It should be clear to readers that Myers and Sang developed the specific architecture, whereas Niven evaluated and compared different architectures When referencing material sourced from the World Wide Web, it is often unnecessary to cite the URL of the site or page; for instance, when the URL points to an online resource, the citation can emphasize the author, title, and publication details rather than the URL itself.

Access the database of scientific data at http://www.sander.embl-heidelberg.de/hssp/ Typically, a published article or paper accompanies the database and describes its scope and use You can then reference the database by citing the accompanying publication, as illustrated by the example reference.

Example profiles were collected from the HSSP database 2 (Schneider et al., 1997).

The cited paper by Schneider et al is a journal article describing the HSSP database For best readability and SEO, place the URL either as a footnote or in a separate URL list distinct from the bibliography Including URLs in the main text is generally discouraged unless the address is short enough not to disrupt the reading.

In Chap 14 we describe different reference styles in more detail, as well as what to think about when structuring your report.

Improve your Learning (and Grade)

Searching for appropriate references can be both rewarding and frustrating; the right sources illuminate a topic, while irrelevant results can derail progress What many students don’t realize is that their underlying beliefs about information seeking subtly shape how they learn—affecting how they search, evaluate sources, and integrate evidence into their writing By examining and refining these views, learners can improve their research efficiency, find more relevant references, and enhance learning outcomes.

Briefly, you can approach your information seeking activity with one of the following views:

● That the main reason for information seeking is to find facts that can be used in the thesis.

● That the main reason for information seeking is to evaluate and analyze previous work.

In information seeking and learning, Limberg (2000) found that students who rely solely on a fact-finding approach produce less qualified results than those who treat information seeking as evaluating and analyzing a complex issue Therefore, adopting an evaluate-and-analyze mindset—not just gathering facts—can lay the foundation for stronger research quality and higher thesis grades.

To evaluate and analyze a complex issue could for example mean that:

● You find information from a variety of sources that provides you with different perspectives on the topic, placing it in a wider context

Scrutinizing information lets you uncover and map the underlying values and motives behind information sources This goes beyond knowing that a problem has been researched and focuses on how it was investigated—what questions were asked, what methods were used, and what assumptions guided the work By bringing the research process into view, you widen the perspective, enhance transparency, and deepen understanding of the topic.

Let us illustrate the above by a short scenario in which you take a first step towards finding information for your thesis project.

Search engines like Google are often the quickest way to find information and can be very effective for computer science topics However, relying on search results alone can be limiting, because many scholarly articles are published in journals and conference proceedings, and full texts are not always freely accessible As a result, you may locate references via a search engine but be unable to read the actual documents, or encounter draft versions that differ from the officially published papers For thorough research, supplement search results with academic databases, check whether items are open access or paywalled, and verify that you’re citing the final published version.

If you can't find the right documents using a standard search engine, explore the bibliographic databases available through your university library These databases are typically accessible via web interfaces and often provide more precise, scholarly results than general search tools.

Bibliographic databases index a vast number of documents published under different circumstances, ranging from journal articles to conference papers (often listed as conference proceedings) When you search, you must decide between journal articles and conference papers, and consider whether one venue offers higher value or credibility than another Could articles in a given journal be more valuable than those in a different journal? How can you assess whether a particular author is highly regarded in the field, and what role do author affiliations play in judging quality? Is it reasonable to assume that a paper originating from a prestigious university is of higher quality? Answering these questions takes practice and experience in evaluating sources, understanding publication norms, and weighing factors such as venue reputation, author track records, and institutional affiliations in context.

Paying attention to the journal or conference where an article is published matters, because it helps you assess credibility and decide whether you’d want to follow that publication in the future Consider who the author is and whether they have published more on the subject, and examine the affiliation details to see if the author is part of a research group with other documents that might interest you A search engine is often an excellent tool to identify and locate people and to uncover connections between authors and related works It’s also useful to notice whether there’s a link between the kinds of articles you find and the database you used Collectively these factors form the contextual aspects of information seeking—elements that novices frequently overlook.

Contextual factors matter, but the text itself is the primary object of evaluation in academic writing; the value of an article should be judged by its content rather than the author’s fame or the institution’s reputation An article from an unknown author or from an institution with uncertain standards can be just as strong as work by a renowned expert if its arguments are solid Critical evaluation questions include whether the text is logically coherent, whether the arguments are well grounded, whether the study is methodologically sound, whether data are analyzed in a relevant and correct way, and whether there is a credible correlation between the results and the conclusions.

The issue of information seeking is discussed in additional detail in Chap 13.

Start by defining the project aim clearly to know exactly what you want to achieve and to communicate effectively with your supervisor, examiner, and others involved This clarity helps keep discussions on track, as both you and your interlocutors share a precise understanding of the project’s purpose, scope, and goals, ensuring everyone stays aligned on what the project is about.

Having a clear aim is essential for evaluating the usefulness of your project With a well-defined objective, you can present your project more effectively, and anyone you share it with can quickly determine whether they are interested in the expected outcomes.

Starting your project with a clear, well-defined aim at the outset makes it easier to evaluate the eventual outcomes In the evaluation phase, the examiner checks whether the project has fulfilled its stated aim, so articulating the aim early clarifies expectations and demonstrates how success will be measured By developing the aim at the beginning, you also reveal the demands your supervisor and examiner will place on a successful project.

Therefore, once your project proposal has been accepted, you should contact your supervisor as soon as possible, in order to discuss necessary refinements to your aim.

Meetings with Your Supervisor

Meetings with your supervisor are stimulating events where knowledge is created!

The meetings with your supervisor are opportunities for you to present your ideas, get valuable feedback on those ideas, and to discuss all aspects of your project.

Productive meetings hinge on preparation and efficient time use; arrive focused and ready by drafting in advance the questions you want to ask your supervisor, ensuring you cover the key topics and secure clear answers.

During the initial meeting with your supervisor, the primary aim is to agree on the project's objectives The submitted and accepted project proposal, possibly accompanied by examiner comments, serves as the foundation for that discussion, guiding how to define scope, deliverables, and milestones Together, these elements shape the direction of the work and ensure alignment between you, your supervisor, and the assessment criteria.

M Berndtsson et al (eds.), Planning and Implementing your Computing Project - with Success! © Springer 2008 input to this first discussion At the first meeting, we recommend that you agree with your supervisor on how you would like to work together during the project:

Regular meetings are recommended to keep teams aligned, with a common example being a weekly 1-hour session The frequency and duration of meetings should follow your department's guidelines, or, if not specified, be decided by the supervisor according to their schedule.

Local guidelines can influence how often meetings occur and how long they last, with significant variation across departments Regardless of these differences, it's important to determine how often you can meet with your supervisor and to come prepared for every meeting.

Set up how and when to submit materials for feedback from your supervisor—whether by email or hard copy—and agree on a submission deadline, such as three days before the meeting If you want your supervisor to read and comment on your writing, submit it at least a couple of days in advance to give them time to review The exact arrangement depends on both your and the supervisor’s schedules, and these details should be agreed during the first meeting.

In every meeting, take the initiative and contribute ideas instead of expecting your supervisor to drive all the creative thinking for the project Avoid simply asking what you should do next; come prepared with ideas and options Your supervisor can then provide feedback on your proposals, helping refine your approach through constructive input and fostering collaborative project management.

Time Plan

Developing a project time plan provides a clear view of how key dates, activities, and the duration of each task relate to one another, enabling accurate scheduling and informed decision-making A well-constructed timeline helps mitigate risks and prevents problems such as missed deadlines by aligning milestones with realistic task durations and available resources.

When you start your project, your supervisor and examiner will probably already have informed you of a number of important dates These may be, for example:

● Dates for submission of drafts

● Date for submission of first complete draft

● Date for submission of final report

These dates mark the milestones of your project and should be entered into a time plan, ideally using a computer-based tool Once the key milestones are in the plan, you can add project activities, estimate the time each activity will take, and define the relationships between activities—such as overlapping or sequential dependencies—for a clear project timeline When you develop the first version of the time plan, you can refine durations, adjust schedules, and build a practical roadmap to guide execution.

Activities to Perform While Developing the Aim

At the outset, you may not know all the activities you will need to carry out, and that's okay Start with a broad description of activities—such as following objectives—and then, as you develop the project's objectives, refine these into a concrete set of specific, actionable tasks This iterative approach keeps planning flexible and ensures that each activity aligns with the overall goals once the objectives are clear.

As your project progresses, you can enter into the time plan a number of more specific project activities such as:

● Dates for preparation of oral presentation

● Dates for preparation of draft chapters

● Dates for completion of phases or objectives

● Dates for meetings with supervisor

Accurately estimating the time required for each activity is the core challenge in building an effective time plan To improve accuracy, continuously update your timeline as work progresses and refine estimates accordingly Regularly discuss the plan with your supervisor to obtain feedback and adjust assumptions to reflect real conditions This iterative approach helps manage expectations, allocate resources wisely, and keep the project on track By maintaining an up-to-date, transparent time plan, you can reduce overruns and deliver predictable results.

You can manage and visualise your time plan with a variety of diagrams, including activity networks and Gantt charts Your university library is likely to offer books that explain these diagram types in depth, alongside broader guidance on project planning.

7.3 Activities to Perform While Developing the Aim

Figure 7.1 provides a concise map of the activities involved in developing your project aim, with no fixed order required The process is flexible, allowing you to adjust the sequence as you work Depending on the literature you consult for your project proposal, you may refine the aim, craft a clear overall introduction, and build the rationale and supporting arguments for the project aim by integrating that literature.

In most cases, however, developing the project aim will mean that you need additional literature to support the motivations behind the aim of the project, which

Refine the initial aim Develop the arguments behind the aim

Fig 7.1 Activities to perform when developing the aim means in turn that you probably should start searching for additional literature first

In the following sections, we elaborate on each of these activities.

An effective project aim is a concise, unambiguous sentence that states the overall goal of the project Most teams already draft a preliminary aim in their project proposal, but choosing the exact wording can be tricky Now is the time to refine that aim into a clear, solid description of the project's goal, ensuring it is specific, measurable, and aligned with stakeholders’ expectations This refined aim acts as the anchor for planning, evaluation, and communication throughout the project lifecycle.

When undertaking an e-commerce project, you can start with three illustrative forms of your initial aim These three example aims show different ways the first draft can be shaped, and each will be refined later in this section as you deepen the project’s scope and objectives.

Aim 1: Develop an infrastructure for electronic commerce

Aim 2: Investigate security issues for electronic commerce

Aim 3: Investigate the use of electronic commerce for product marketing

It is important that you check and evaluate every word of the aim Including:

● Are all words clear, or can some words be interpreted to mean different things?

Ambiguous wording invites misinterpretation and undermines clear communication, leaving readers likely to misunderstand your intended purpose That same ambiguity can also make you question your own message later in the project, as you struggle to articulate the true goals you are trying to convey.

● Does your aim promise too much? Try to find the right level, so that the aim of the project does not become too simple or too difficult A project that runs over

6 months and a project that runs over 12 months should not have the same aim Will you be able to accomplish what you have promised in the aim within the allowed time frame of the project?

When defining the project scope, consider whether restrictions on the aim—such as area, region, or time periods—are appropriate A common pitfall is allowing time constraints to dictate what can be studied, for example, “due to time constraints in this project, concept X will not be investigated.” This raises a key question: is the restriction a valid limit on the scope, or does it signal that the aims are too ambitious for the allowed timeframe? Evaluating feasibility, expected deliverables, and risk helps decide whether to narrow the scope, adjust the timeline, or recalibrate expectations to keep the project coherent and achievable.

To ensure clarity, every concept used in the aim should be explained clearly, with all important terms defined or explained early in the text Presenting concise definitions at the outset helps readers understand the discussion from the start and improves SEO by aligning content with user search intent.

By considering the above guidelines we could now refine the three example aims mentioned above into:

Aim 1: Develop a security infrastructure for electronic commerce based on

We have here added details concerning the type of infrastructure (security) and that the infrastructure should be based on XML.

Fig 7.2 Arguments behind the aim

7.3 Activities to Perform While Developing the Aim 49

Aim 2: Investigate security issues in negotiation protocols for electronic commerce

We have here added details to show that the project is explicitly targeting security issues for negotiation protocols.

Aim 3: Investigate the usage of electronic commerce for product marketing by small and medium sized companies

We have here added details concerning the size of companies that we are interested in.

Alternative aim 3: Investigate the usage of electronic commerce in for product marketing by small and medium sized companies in Sweden and U.S.A.

We have here refined the aim further by adding two countries that we are interested in studying.

Use a simple test to judge whether you’ve clearly formulated your aim: can you summarize your project in two or three sentences when someone asks, “What is your project all about?” You’re likely to encounter such questions from friends and family, and you should treat them as opportunities to practice and to verify that you truly understand your project If you struggle to explain the aim to people outside your area of focus, it’s a sign that your understanding may not be as clear as it should be, or that the aim needs refinement.

7.3.2 Develop the Arguments Behind the Aim

The project aim needs to be supported by proper arguments which explain why it is important to investigate the topic.

Figure 7.2 shows that your aim can be supported by a number of arguments that either directly (argument 3 and 4) or indirectly (argument 1 and 2) contribute to

Identify supporting literature that explains why the study is undertaken, focusing on sources that align with the core theory and clearly connect to the aim Build a concise rationale by drawing direct links between theoretical concepts and the research question, showing how prior work fills a gap or extends understanding in the field Ensure the literature review demonstrates explicit ties to the aim, using evidence from previous studies to justify the design, scope, and expected contributions of the research By articulating how existing theories and findings underlie the aim, the paragraph provides a coherent foundation for the study and enhances discoverability through precise terms such as rationale, theoretical framework, supporting evidence, and research aim.

Investigate the usage of electronic commerce in for product marketing by small and medium sized companies in Sweden and U.S.A.

To justify pursuing this aim, the motivation behind studying electronic commerce is threefold: first, e-commerce is an increasingly important field that most companies should consider, with valuable lessons available from early attempts to implement such systems; second, the focus on small and medium-sized enterprises (SMEs) is deliberate because many lack in-house knowledge to deploy e-commerce effectively; and third, examining efforts in Sweden and the United States matters, since 1990s reports suggested the two countries were at similar levels, and a current comparative analysis could reveal whether that parity has shifted.

Strengthen the significance of your aim by grounding it in three relevant, credible references from peer‑reviewed sources that directly support your arguments If a scientific journal article or conference paper presents a well‑argued claim about investigating the usage of early electronic commerce systems, include that citation and connect it to your objective This approach shows that other researchers share the view that studying early e‑commerce usage yields insights, provided the cited work offers solid reasoning Select three sources that illuminate the claim from different angles—theoretical justification, empirical evidence, and implications—and integrate them with in‑text citations to reinforce the narrative and optimize SEO with keywords such as early electronic commerce systems, e‑commerce adoption, and scholarly evidence.

Important Concepts

After defining your project aim, you can begin formulating objectives and assign a method to each objective Figure 8.1 shows the relationship between the aim, objectives, and methods A project has one overall aim, supported by several objectives Each objective is a small, achievable and assessable sub-goal that, when fulfilled, contributes to satisfying the overall aim.

Each objective can be achieved through different methods, and a method is a systematic approach to solving a problem In planning a project, you should identify, for every objective, an appropriate method to reach it You don’t have to apply the same method to all objectives; different objectives may require different methods Using a mix of methods can make validity more complex, and the chosen methods affect both the quality of the data you collect and the conclusions you can draw about the overall aim To illustrate the relationship among aim, objectives, and methods, imagine you have a green car and you want to reach a specific destination You would select routing options and driving approaches aligned with each objective, while ensuring the overall plan stays coherent and viable.

M Berndtsson et al (eds.), Planning and Implementing your Computing Project - with Success! © Springer 2008 like to change the colour of your car to red For this project, we can identify “change colour of car to red ” as the aim of the project Given the aim, we can formulate a number of objectives as sub-goals:

● Remove parts that should not be painted

● Re-assemble removed parts once the paint is dry

Certain objectives require actions to be performed in a specific sequence—for example, buying paint must come before painting the car Other objectives do not rely on order and can be completed in any arrangement, such as removing parts that should not be painted and buying paint.

Let us consider the objective: “Paint car” There are different methods we can apply for painting the car For example, you can paint your car by:

4 Paying the neighbour’s son to do the job

All four painting methods can be considered viable options, but the finish quality varies by method Method three is likely to deliver the best overall quality, while method one may change the color of your car but produce a poor-quality finish Method four carries the greatest uncertainty, with the final result being difficult to predict.

This simple car-painting example demonstrates that the quality of project results can range from poor to excellent, depending on the methods you choose and how proficient you are at applying them The key takeaway is that selecting the right method and executing it with skill are the main drivers of success in any car-painting project.

Fig 8.1 The relationship between aim, objectives and methods

Addressing Validity and Reliability

When you are developing objectives and selecting methods, consider how validity and reliability might affect your project Validity denotes the degree to which what you intend to measure (or examine or develop) corresponds to what you actually measure (or examine or develop) Reliability refers to the accuracy and consistency of your method—how robust your approach is in measurement, including implementation, questionnaires, and interview styles.

A method is valid and reliable only within a certain range of uses For example, a tape measure provides high validity for measuring a person’s height, but it is not suitable for determining the height of Mount Everest Additionally, a tape measure is not especially reliable, because the tape’s elasticity can cause it to stretch and introduce measurement errors.

Compared with alternatives, a steel ruler provides greater reliability because its rigidity prevents stretching Consequently, the method you choose for your project remains valid and dependable only within a defined range of use, beyond which accuracy and trustworthiness may diminish.

By considering validity and reliability when selecting your objectives and methods, you lay a solid foundation for the later stages of your project, including how you present and analyze your data This careful approach boosts the overall quality of the project by supporting robust study design and clearer data interpretation.

We continue our discussion by looking at two important threats to validity which are of particular concern in qualitative research.

Bias presents the first threat to validity, especially when researchers cannot account for their own preconceptions In organizational research, it is almost inevitable to act on prior theories, values, and views, which can shape thinking, actions, and interpretation of findings throughout the study These influences can color how you perceive, observe, and interpret data To mitigate this threat, provide a comprehensive description of your background and theoretical stance during the planning and reporting of the study, and explicitly acknowledge how your perspective may influence design, data collection, and analysis By accounting for bias, you reduce the risk of selecting an organizational context or methods that merely justify preconceptions, thereby enhancing the study’s credibility for readers.

Another potential threat to validity arises from the researcher's influence on the setting under study, especially in projects that involve interviews and data collection Changes in the environment during the study—often subtle and not easily observed—can affect findings, particularly when tacit social and human dynamics are involved When conducting a series of interviews, interviewees may revise their views on certain issues as they encounter new information or as the project unfolds within their organization To minimize this threat, researchers should acknowledge that any study will influence the organization to some extent and actively monitor and document these changes throughout the research process.

Returning to the same interviewee for an additional session may mean they view issues differently as the project evolves, so be prepared for shifted perspectives In qualitative research, it is hard to precisely identify and quantify the influence of every individual factor Nevertheless, recognizing that changes can occur and staying sensitive to them throughout the study is essential Acknowledging the existence of these influences and maintaining a comprehensive understanding of the setting are prerequisites for properly accounting for and addressing potential threats to validity.

In qualitative research, although it may seem desirable to eliminate all preconceptions, researchers generally acknowledge that the rich data obtained through close observation and interpretation by the researcher are of paramount importance A more productive approach is to recognize the existence of bias and to account for it in the analysis of the research findings.

These additional examples illustrate why addressing threats to validity and reliability is essential in any research project An inadequate treatment of these threats can lead readers to question the overall quality of your work For example, if you have conducted interviews but have not allowed interviewees to review your transcripts, you cannot confidently claim what the interviewees actually said, since there is no evidence for readers that the transcript accurately reflects their statements In such cases, examiners may question your data collection and data analysis processes.

In another project, you may have used questionnaires to collect data, and if you developed the questionnaire yourself, you may find that many respondents misinterpret the questions once you start collecting responses Such misunderstandings can distort the results, weaken data quality, and mask true patterns, underscoring the need to pilot test the survey, simplify wording, provide clearer response options, and revise items based on feedback before full deployment.

An examiner may scrutinize your entire study when the data you collected diverges from the planned approach, signaling that the subsequent analysis may be unreliable In this scenario, the questionnaires should have undergone pilot testing and revision before distribution to respondents.

To build a tool that measures database query response time for SQL queries, you quickly develop a prototype and begin collecting timings across different query types However, an examiner could question the project if the tool only measures a portion of the full query execution, leaving gaps in performance data The tool may also be sensitive to ad hoc inputs, for example crashing whenever a query includes the word 'salary', which undermines reliability A credible assessment requires measuring the complete execution path and ensuring robustness against such edge cases, so results reflect true query performance rather than partial or brittle measurements.

Using a systematic approach for literature analysis is essential to ensure the relevance and validity of the material you collect Without a systematic search, you risk missing key sources, which can weaken the credibility of your report and its conclusions A transparent, comprehensive description of the literature search strategy enables others to assess, reproduce, and extend your analysis, and helps ensure that all relevant sources are considered.

8.2 Addressing Validity and Reliability 57 assess the findings Thirdly, as new material is continuously being published, there is a problem in knowing which sources have already been considered Therefore, if the study has a long time-span, you run the risk of either omitting recently published material, or having to redo an extensive amount of the literature analysis.

To ensure readers trust that the literature analysis covers all relevant material, plan the relevance assessment and source selection carefully, and document how you address the question of relevance within your literature analysis Maintain adherence to your stated research strategy to preserve focus and consistency If deviations arise, acknowledge them openly and provide clear motivations to maintain transparency In such cases, expert opinions, including guidance from your supervisor, can help ensure that important work is not overlooked and that the analysis remains comprehensive.

Methods

In the literature, there is little consensus on what counts as a method and what counts as a technique of data collection, with techniques referring to the various ways of collecting, interpreting, and analyzing data In this book, we do not aim to resolve the distinction between techniques and methods Instead, we take a pragmatic approach and present a set of methods and techniques that we consider appropriate for addressing the problem at hand.

Literature analysis is a systematic examination of a problem that rests on the analysis of published sources and is undertaken with a specific purpose in mind This approach should not be confused with a literature review, which is conducted to familiarize oneself with what has already been done in a field In practice, literature analysis uses published evidence to answer a focused question, evaluate arguments, and identify gaps, while a literature review surveys existing work to map prior findings and set the context for new research.

Literature analysis is a common requirement across projects When you develop a genome‑data analysis program, you typically contrast your tool with similar programs reported in the literature; in a broader literature study on benchmarking database transactions, the emphasis shifts to analysing existing work—categorising it and detecting recurring patterns To address the reliability and validity concerns that affect literature analyses, we include a dedicated section on these issues, since they are relevant to most projects.

As noted earlier, when conducting a literature analysis it is essential to recognize the potential consequences of the chosen search and material collection strategy, and to perform careful interpretation and systematic analysis of each source to ensure reliable, meaningful findings.

After defining the problem and objective, the next step in literature analysis is selecting sources To ensure a rigorous review, identify sources that are relevant, credible, and directly address the research questions while achieving sufficient coverage of the topic This involves establishing explicit inclusion criteria for scope, timeframe, language, and discipline; evaluating credibility, bias, and methodological quality; and prioritizing peer‑reviewed articles, authoritative reviews, and primary studies A balanced mix of foundational theories and recent findings supports triangulation, avoids gaps, and strengthens interpretation Employ systematic search strategies, citation tracking, and transparent documentation of the source selection process to produce a reproducible, SEO‑friendly narrative.

To identify the sources most relevant for a literature analysis, use a range of techniques and, when helpful, combine them to ensure comprehensive coverage of the topic Build a search strategy that includes database queries, citation tracking, keyword and synonym exploration, and screening of abstracts and conclusions, then apply clear inclusion criteria such as relevance to the research question, methodological fit, and publication quality Examine reference lists and forward citations to uncover additional sources, and periodically repeat searches to capture new scholarship and terminology shifts across related fields.

Bibliographic databases are most effective when you apply a thoughtful search strategy: start with a core set of keywords identified from your background reading and the problem statement, then search or browse to uncover relevant sources Use references from a given source to locate additional materials and expand your literature by tracing citations, enabling you to discover related studies, reviews, and datasets This approach helps you build a comprehensive, high-quality collection of literature that supports your research while efficiently widening the search through linked sources.

Journals and conference proceedings can be accessed by browsing the table of contents or by using the search function on a journal’s website to locate relevant articles Most scientific journals are published in both print and electronic formats, and libraries that subscribe typically provide free access to the electronic version.

Deciding when a literature review has collected enough material is a core challenge: you can never be certain that all relevant sources have been considered, yet stopping too early can undermine the validity of your findings By clearly outlining your data-collection process and source-selection strategy, you help readers understand why certain sources were included and why others were left out, which in turn builds trust in your conclusions The required level of completeness depends on the study’s aims and the specific angle of the phenomenon, but adopting a systematic and transparent approach to coverage generally strengthens the credibility of the research When readers can see the rationale behind inclusion and exclusion decisions, they are more likely to view the study as thorough, balanced, and well-supported.

Expert input, including your supervisor’s perspective, helps confirm the feasibility of your strategy, especially when the process is lengthy and you’re confronted with a mountain of material External recommendations can help you decide whether to continue, and you may need to rethink your approach at certain points as new findings emerge to preserve relevance and completeness Having someone monitor progress—able to suggest planned or unplanned deviations—can keep the project on track If you do deviate from the plan, you should be able to justify the reasons with solid evidence.

In literature analysis, it is essential to acknowledge your initial interpretations of the phenomena under study To protect the validity of your findings, engage in a self-reflective examination of your own behavior throughout the research process, recognizing how your preconceptions might influence the analysis.

For the results of your study to be trustworthy, it is important that you address both the process and outcome of the analysis properly With respect to the process,

8.3 Methods 59 you should emphasise both your strategy (i.e what you planned), and the process of data collection and analysis (i.e how you actually undertook your study) In addressing the issue of validity of the findings of your study, this is of key importance Similarly, it is important that you acknowledge the sources on which your analysis is based You should consider carefully why, and to what extent your sources are appropriate and relevant as a basis for your study.

Interviews can be conducted using different methods and formats to fit diverse research goals Choosing the appropriate interview form for your study involves weighing the strengths and weaknesses of each style and how well it aligns with your research design and your own interviewing abilities Different interview styles offer distinct advantages and trade-offs, and the right choice depends on the questions you want to answer, the depth of insight you need, and the researcher’s skills Plan carefully for preparation and execution, covering question design, consent and ethics, recording, and ways to build rapport to improve data quality With thoughtful selection and thorough preparation, the interview method you choose can generate reliable, actionable data within your study’s timeline and resources.

An open interview is a qualitative research method in which the interviewer grants the interviewee substantial control over the issues discussed during the session, with topics not pre-planned and the direction determined by what the interviewee emphasizes Questions should be open-ended and non-leading, allowing the interviewee to express their ideas in their own words This approach aims to uncover issues that are truly important to the interviewee, making the interviewee’s perspective central to the findings However, it can be challenging for inexperienced interviewers, who must balance genuinely open questions with targeted probing to keep the discussion aligned with the study’s overall purpose The open style can also complicate note-taking, since the conversation can be less structured and more exploratory.

Related to these considerations is the need to address the issue of validity properly, and the especially relevant issue of researcher bias.

A closed interview, also known as a pre-structured interview, uses a fixed set of questions asked by the interviewer and does not allow adding or deleting questions based on responses; this structure offers strong repeatability and is well-suited for statistical analysis in survey research, but some questions may seem only marginally relevant to the interviewee, potentially reducing motivation to provide complete responses.

Preparing and conducting interviews includes strategies for:

An Illustrative Analogy

To highlight the distinct characteristics of each method, we use a sports analogy that readers can easily relate to This analogy helps clarify how different methods work and when they are best suited for solving various problem situations By connecting methodological choices to familiar athletic strategies, we offer a practical framework for evaluating options and deciding which approach to apply in real-world practice.

This analogy uses multiple scenarios to illustrate key ideas, acknowledging that some elements may be questioned for realism while emphasizing that they still offer useful illustrations For example, convincing a team's players to participate in the large number of matches needed for a properly designed experiment—one aimed at achieving statistical significance—would be difficult, but such illustrations help explain the concept.

Background – Context and actors in domain Suppose that there is a soccer team playing in a soccer league (i.e an organised competition), so that they will compete with a number of other teams in the league on a yearly basis In this analogy, we will refer to the specific soccer team as “Hopeless United” The team consists of a set of players, and a coach Among other duties, the coach decides on the strategy and tactics by which the team should play, and is responsible for the overall performance of the team (and consequently, also for each individual player’s performance) The owners of the team have financial responsibility and are keen to receive some return on their invested money Finally, the local newspaper writes a number of articles on Hopeless United.

Background to a study on the team Last year, the team performed badly, and the different actors have a number of different approaches to how to tackle the problem of identifying factors that can improve the team’s performance.

In this analogy, all actors share a common objective: identifying factors that can improve the team's performance We now examine how each actor works toward achieving this aim, and how their distinct approaches contribute to boosting team performance.

Scenario 1 – Owners The owners are interested in what can be done in order for the team – as a whole – to improve their overall performance This is based on an assumption that if the team plays well, there will be more spectators interested in watching the team’s matches, with a consequential increase in funding from the sponsors of the team Thereby, the individual performance of individual players would (in general) not be of specific interest One obvious exception would probably be that of superstars, since if the team has such players, they would most likely be very popular amongst the spectators and fans At a meeting two potential approaches emerge.

Investing in two promising new players—a goalkeeper and a forward—could boost the team's performance by increasing saves and goals The owners expect the new forward to score more and the goalkeeper to make more saves, translating into better results on the pitch After one season, they will evaluate the team's performance to decide whether the new players should stay with the squad.

We could characterise this approach as a form of experiment during which the new players are tested.

An alternative strategy is to seek guidance from external experts in the field This can be treated as a case study, since only a small number of specialists are likely to be consulted, allowing the team’s current situation to be examined and interpreted through existing theories, each expert bringing a distinct view of how soccer should be played An open-interview format is preferable to capture the core recommendations these experts offer on improving the team's performance In Scenario 2, the coach has spent several weeks working to determine the best course of action, and three potential approaches for addressing the situation have emerged.

To gain an overview of what might be wrong and how the team can be improved, the coach seeks external feedback because he feels too close to the daily environment He plans to gather input from current and former players as well as members of the local supporters’ club, framing this as a survey to capture diverse perspectives With input coming from many respondents, data would be collected through questionnaires, and those questionnaires would be anonymous to ensure players and others can express their opinions freely.

The coach could enlist help from nearby university researchers who have developed a computer program to test different cooperation strategies among software agents, using a soccer simulation game as the test environment The aim is to build a software application that can illuminate various aspects of Hopeless United's strategy and to verify the hypothesis that a particular tactic yields more goals This effort can be seen as an implementation—developing a working program to demonstrate key elements of the strategy—and as an experiment—systematically adjusting the players’ tactics to confirm or refute the goal-scoring hypothesis.

Finally, the coach could shift the team's current formation—from two forwards, four midfielders and four defenders—to a new setup such as three forwards, four midfielders and three defenders With this tactical adjustment, the team can play in the updated formation, potentially boosting attacking options while maintaining defensive balance.

8.4 An Illustrative Analogy 67 formation for ten matches, and investigate whether there is any improvement in performance We could characterise this approach as a form of experiment, since the coach plans to test a new formation over ten matches, and then investigate whether the new formation has improved the team’s performance.

Scenario 3 – Local newspaper The local newspaper plans to run a series of articles on why the team wins so few matches and what can be done in order to improve their performance For these articles, they intend to go through their archive of articles and books on Hopeless United The purpose is to provide a historical overview of how the team has evolved over the years, and investigate whether suggestions for improvement can be identified based on this historical overview We could characterise this approach as a form of literature analysis, since many literature sources (books and newspaper articles) will be analysed.

These scenarios converge on one goal: identifying the factors that can improve team performance The approach varies with perspective, revealing a broad range of methods to reach this aim Even when focusing on a single actor and a single situation, more than one method can be applied to reach the aim A single method alone is rarely ideal; often, a combination of methods yields the best results for boosting team performance.

A Four-Step Process

Here we present an overview of four steps involved in identifying and describing your project’s objectives and selecting suitable methods to fulfill those objectives, guiding you through clear goal definition, precise objective articulation, and method selection to align activities with desired outcomes and improve project success.

Once you establish a clear aim for your project, plan how to achieve it by defining sub-goals (objectives) that progressively lead you toward the aim By fulfilling each objective you move closer to the overall goal, and for some objectives it helps to write down the underpinning argument or rationale to clarify why that objective is being tackled.

The scope of objectives varies from one institution to another Some institutions require every task you perform, such as writing the report, to be described as an objective, while others only require that you frame the practical work in terms of objectives In practice, most thesis projects do not require more than three to four objectives.

Here follows an example of an aim and its set of objectives.

Aim: Compare the current theory on X with how companies support X in practice.

● Select companies Identify ten companies that work with X in practice These companies should have been working with X for at least 5 years.

Start by selecting robust comparison criteria to enable a meaningful comparison between the current theory of X and the current practice of X The criteria should be clearly defined, relevant, and measurable, so theoretical assumptions and practical outcomes can be evaluated on the same basis If existing comparison criteria are not available in the literature or other sources, they must be invented using principled methods, informed by domain knowledge, stakeholder needs, and observable indicators Apply this transparent criteria framework to both theory and practice to support rigorous evaluation, evidence-based conclusions, and actionable recommendations.

● Investigate how companies work on X with respect to the comparison criteria.

● Analyse the results obtained from the companies and compare with what is being said in the literature on X.

Once you have written down your objectivities, you can start to identify potential methods for each objective Think in terms of: how can this objective be achieved or solved?

Reconsider your objective: investigate how companies operate on X with respect to the chosen comparison criteria This objective can be achieved using a range of methods, such as questionnaires or interviews, which should be broken down into specific types of interviews as needed Write down only those methods that are relevant to your objective, in the context of your aim, and avoid listing irrelevant methods merely to show a large number of alternatives.

Once you have identified a potential method, briefly describe how it could be used to meet your objective and what the implications might be for your final result

In our example, you could describe the type of material collected through an interview—such as quotes, anecdotes, data points, and expert insights—and explain how the depth, relevance, and reliability of that material influence the quality of the final project By outlining what is gathered, how it is gathered, and why it matters, you set a clear basis for analysis that strengthens conclusions and supports a compelling narrative Considering how different sources introduce bias or add context helps tailor the interview process to maximize credibility, relevance, and resonance with the intended audience, ultimately shaping a stronger final result.

8.5.3 Choose Among the Potential Methods

After identifying a set of potential methods to achieve the objective, you should select the method (or methods) you will actually apply, with a clear justification for each choice Every option comes with its own advantages and disadvantages, so evaluate these trade-offs to pick the approach most likely to yield the highest-quality final result Avoid methods that produce low-quality data, as data quality directly impacts the overall outcome of your work Consider factors such as feasibility, cost, and potential impact to ensure the chosen method aligns with your goals and delivers reliable results.

Do not make choices with arguments such as “due to time constraints, method

X will not be chosen as a method” An examiner or supervisor can then respond with an argument such as “you have identified and described a method that not

8.5 A Four-Step Process 69 even in theory can be applied within the allowed time span of the project” What you can do is to mention the method that culd not be used due to time constraints in your discussion about future work Perphaps the method that you could not use in your project can be used in a follow-up project in the future.

8.5.4 Present Details of the Chosen Approach

Once you have selected the methods you will use, summarizing your overall approach provides a bird’s-eye view of the chosen methods and their relationships, and it lets you outline how you plan to apply each method within your project A clear summary makes it easier for readers to grasp your strategy and to understand how you intend to structure and execute your work.

After your problem description passes quality control (see Fig 4.1), you can begin the project execution As long as nothing unforeseen occurs, your next steps are to follow your objectives, which will guide you toward achieving the intended aim.

During a project, new findings may arise that were not anticipated when the aim was defined, potentially pulling the work away from its core objectives To stay aligned, refer back to the original aim and decide whether the new issue fits within the project scope or should be proposed as future work If needed, update the project objectives so the new element can be integrated smoothly within the plan Always discuss any proposed changes with your supervisor before adjusting the aim, since changes made without prior discussion may not be well received and could alter motivation, implications, and outcomes The agreed-upon aim serves as the common goal and contract guiding the project.

Regular meetings with your supervisor and the examiner are key checkpoints to monitor your project’s progress and gather actionable feedback Taking these sessions seriously helps keep the work on track and can prevent future problems.

When you find yourself unable to meet your objectives, discuss the situation with your supervisor immediately This article provides practical advice for both students and supervisors on how to handle these scenarios, and it introduces a risk scale from 1 to 3, where 1 indicates low risk and 3 denotes a high risk of rejection by the examiner.

Additional work (risk level 1) examines whether adding further experiments, simulations, interviews, or reading—aligned with your chosen method—can generate extra results that meet your objectives and help you draw clear conclusions from your project.

One key risk is that additional work may take too long to complete; starting tasks you don’t have time to finish wastes valuable time that could instead be spent analyzing and writing up the conclusions drawn from your existing results.

Presenting Non-Numerical Data

In this section, we present guidelines for presenting non-numerical data such as:

10.1.1 Presenting Data from a Literature Analysis

When you present your literature analysis, keep its purpose at the forefront of your project, because that purpose drives how you structure the entire presentation If you use literature analysis as the method to identify differences between two software engineering methods (for simplicity, methods A and B), let the outline highlight those contrasts with clear criteria, concise summaries, and compelling evidence drawn from the literature This alignment—purpose guiding structure—creates a coherent narrative that helps readers understand the distinct approaches, outcomes, and trade-offs of each method while ensuring the discussion remains focused and evidence-based.

M Berndtsson et al (eds.), Planning and Implementing your Computing Project - with Success! © Springer 2008 method X and method Y), you can structure the presentation of the literature analysis in a number of different ways Here follows two examples:

1 You can begin with a detailed description of method X, including quotes and excerpts from the literature you used You should report any inconsistencies in the literature, for example, if some authors claim that method X is not suitable for a given type of system, whereas other authors claim the opposite You should report the arguments that were used by each author to support his or her claims The description of method X can be followed by a detailed description of method Y, using the same technique You can then report what is said in other literature that has compared the two methods, if such literature exists.

2 You can begin by reporting what is said in the literature about desirable properties of software engineering methods Again, you should report any differences or contradictions in the views of different authors, and state the arguments they are using to support their views After this you can give detailed descriptions of the methods X and Y, which are to be compared in your study The final part of your literature study can be to compare methods X and Y on the basis of the properties identified earlier You should then report which of these properties are present in each method, and to what degree.

Presenting data from a literature analysis cannot be reduced to a simple catalogue of quotes; instead, it must be organized in a way that serves the analysis's purpose, with a clear structure that highlights how each source supports the research aim This approach requires distinguishing between sources that contribute meaningfully to the analysis and those that do not, and selectively including material that is relevant and useful to the project Generally, discussions or excerpts from sources should be omitted if they are not related to the project or will not impact the work, regardless of how long they were read The time spent on a source is not a criterion for its inclusion, so focus on relevance and contribution to the literature review By continually keeping the literature-analysis purpose in view, you can ensure a coherent, efficient, and impactful presentation of the sources.

Literature analysis is often structured to articulate a clear argument or interpretation, with that argument supported and illustrated by material from the literature, while also integrating the most important counter-arguments you encounter For example, if your project investigates the usefulness of neural networks for predicting crop yields and uses literature analysis plus a prototype implementation as methods, you can frame the analysis as an argument for the usefulness of neural networks for this prediction task, drawing on agricultural studies, general neural network literature, and related sources to bolster the case To maintain balance, include key counter-arguments and limitations alongside your supporting evidence Two practical structures for this literature analysis include: (1) present the rationale first, then marshal supporting literature and empirical examples to back the claim, followed by counterpoints; or (2) outline competing viewpoints up front and then develop a reasoned case for the usefulness of neural networks, integrating evidence from multiple domains to address those viewpoints.

1 Start by describing in detail what properties neural networks have, using technical literature on this subject Continue by describing previous work on crop harvest prediction, including work that has clearly identified and described the reasons for shortcomings of earlier methods This way, you can identify the properties that a method is expected to have in order to predict crop harvests successfully You can then conclude your literature analysis by showing that the properties of neural networks match those identified in the literature as being successful Finally, include any material you have found indicating that neural networks may fail in this task, and describe the arguments stated by those authors.

2 Start by describing in detail previous work in crop harvest prediction, including the literature that identifies the shortcomings of earlier methods Describe in detail the properties of neural networks, as well as previous work on applying neural networks in different tasks Show the similarity of some of these applications with the harvest prediction problem, by reviewing in more detail those parts of the literature that describe properties of the application problems Use citations and excerpts of sections that you believe will convince the reader that a method that succeeds in a particular application should also succeed in crop harvest prediction Finally, include any material you have found indicating that neural networks may fail in harvest prediction.

Literature analysis can be structured in many ways, with no fixed, one-size-fits-all format, so you should tailor the framework to your specific project, the data you’ve gathered, and the arguments you want to advance By looking at examples, you’ll see how flexible organization supports different datasets and analytical goals, allowing you to highlight your thesis clearly and guide readers through your reasoning In short, develop a structure that best fits your research questions, sources, and conclusions to produce a coherent, persuasive analysis.

One of the most effective ways to learn how to present a literature analysis is by studying concrete examples found in the literature you read for your project Examine those analyses to identify what makes them strong, clear, and persuasive, and use them as models for your own writing Bring select examples to meetings with your supervisor, whose experience with both reading and writing literature analyses can offer valuable feedback Have your supervisor confirm whether the chosen analyses are suitable learning models, and if so, discuss one in detail to deepen your understanding By identifying the strengths and weaknesses of another reader’s literature analysis, you can learn practical techniques for structuring your own report.

10.1.2 Presenting Data from Interviews and Questionnaires

After you’ve conducted interviews, you’ll likely have a pile of tapes or notes that together represent your material The next step is to structure and present this material to readers in an appropriate form, which means deciding what to include and how to balance accuracy with readability You can choose from exact word-for-word transcripts of entire interviews to concise summaries of each interview, or a mix that suits your project, and this choice should be discussed with your supervisor to ensure it meets your goals and audience needs.

If you have used questionnaires you probably want to present your data by using figures or tables (see Sect 10.2).

Data from interviews and questionnaires share a common consideration: how you structure the presentation of the collected information Table 10.1 illustrates several ways to organize data from interviews and questionnaires The method you choose may limit the number of structuring options available, and with open interviews it can be problematic or inappropriate to arrange the material in a strict sequential order (for example, question 1 followed by question 2) Therefore, the entries in Table 10.1 should be understood as illustrative examples of possible data-structuring approaches rather than a fixed prescription.

An implementation may be part of your method for several reasons: when you rely on simulation data and need to implement the simulation model or the simulation tool yourself; when you want to demonstrate that a capability can be achieved; or when you are testing a method for systems development In all these cases, applying good software development practices—especially thorough documentation of the implementation—helps ensure clarity, reproducibility, and long-term maintainability.

Since the implementation is an integral part of your project methodology, you should present sufficient documentation to convince readers that the implementation is correct For example, you might have implemented an algorithm and conducted simulations that compare its results to those of an alternative approach, providing evidence of accuracy and performance Clear documentation, rigorous results, and thoughtful interpretation together establish validation of the method and enhance the article’s credibility and SEO relevance.

Table 10.1 Different ways of structuring the presentation of collected data

• Qn Company B • Summary of Q1-Qn

• Summary of company A and B Qn

Company B algorithm: Even if the algorithm has already been described clearly in your report, you must demonstrate that the simulation data are meaningful by proving that the simulations were carried out with a correct implementation of the algorithm Put simply, show that the differences observed between the Company B algorithm and alternatives are not driven by coding errors, but reflect genuine performance differences This validation step is essential for credible algorithm benchmarking and for ensuring reproducible, accurate comparisons of simulation outcomes.

Presenting Numerical Data

In this section, we present guidelines for presenting numerical data, including:

Examining the data in Table 10.2 shows how update time depends on the number of tuples Reading the numbers in the right-hand column indicates that update time roughly doubles for each increment of 10,000 tuples This dependency becomes even clearer when update time is plotted against the number of tuples, as illustrated in Figure 10.3 The graph is a line plot, a straightforward visualization well suited to this data, and it provides an immediate sense of how update time scales with the tuple count.

Table 10.2 shows how update time depends on the number of database tuples for the two algorithms compared in the example project Each update time is the average of ten simulations, illustrating the performance differences as the database size increases.

Tuples (× 1,000) Old algorithm New algorithm

Analysis shows that the update time depends on the number of tuples, increasing exponentially as the tuple count grows The data include values such as 100, 108.2, and 20.3, illustrating this rapid growth pattern and its impact on performance For clear visualization, line plots should place the independent variable on the x-axis and the dependent variable on the y-axis to accurately reflect the exponential relationship.

Figure 10.4 includes a second dependent variable in the line plot that shows update time for the proposed new algorithm, and this addition makes clear that the new algorithm scales better as the number of tuples grows The graph also includes error bars to illustrate the variation in average update time across different tuple counts In the range of 10,000 to 60,000 tuples, the two algorithms perform roughly the same, so the plot does not show a clear overall advantage for the new algorithm within this interval To address this, the 10,000–60,000 tuple range can be plotted in a separate graph, or a log-scale for the y-axis can be used to better reveal differences at lower update times.

Fig 10.4 Line plot showing performance comparison between two database access algorithms

Fig 10.3 Line plot showing performance, i.e update time of the old database access algorithm All update times plotted are averages of ten simulations

Figure 10.5 presents a performance comparison between the new algorithm and the standard algorithm across the 10,000–40,000 tuple range The results indicate that the new algorithm underperforms the standard algorithm within this interval Yet the performance gap is very small, with the error bars overlapping, suggesting that the observed difference is likely due to random variation rather than an inherent difference in algorithm performance.

Choosing which interval to plot in a line graph can unintentionally bias interpretation if the interval is too narrow, potentially giving a false impression For instance, plotting the interval 10,000–40,000 tuples in Fig 10.5 without clearly indicating that this plot complements the whole-interval view in Fig 10.4 may lead readers to think the new algorithm is worse than the old one The same misleading risk exists if only rows 1–4 are included in Table 10.2 Graphs aim to make numerical data easier to understand by visualizing it, as shown in Sect 10.2.1 with line plots of the data in Table 10.2; however, because visualizations can be created in different ways, they can convey different impressions of the data—this variety can support multiple interpretations but also raises the possibility of false or misleading conclusions.

In Fig 10.6, a column plot uses a truncated y-axis that starts at 0.75 instead of zero, creating a misleading graph that makes the performance gap between algorithms look larger than it actually is The distortion is evident at 10,000 tuples, where the bar for the new algorithm is roughly ten times higher than its competitors, producing a deceptive impression of superiority in this data visualization.

Fig 10.5 Line plot showing performance comparison between two database access algorithms Same as the graph in Fig 10.4, except that only the interval 10,000 –60,000 tuples is included

10.2 Presenting Numerical Data 81 for the old algorithm, although the real difference is that the new algorithm takes 1.1 s and the old 0.8 s The new algorithm thus takes 37% longer to execute than the old one, which means that the column should only be 37% higher This discrepancy decreases further up the x-axis, but is present throughout the whole graph At 20,000 tuples, the column for the new algorithm is 55% higher than the column for the old algorithm, but Table 10.2 shows that the new algorithm took only 31% longer to execute than the old one (2.1 and 1.6 s, respectively).

Today’s automated graph-drawing tools enable rapid chart generation and are easy to learn, making them a practical choice for producing visuals for reports These tools boost efficiency but can also produce misleading graphs, such as automatically shortening the y-axis in figures like Fig 10.6 To ensure accuracy, inspect every graph carefully and correct any mistakes introduced by the tool, paying particular attention to axis scaling, labeling, and data integrity Use these tools as a starting point, then verify the output against the underlying data to maintain trustworthy visualizations.

In a numerical comparison where a parameter is varied across simulations or experiments, the system may be stochastic, so outcomes from individual runs can depend on chance To obtain reliable results, repeat each parameter setting many times and average the results across runs A single run can produce an untypical result by chance, so conclusions should not be driven by random effects alone This can be addressed by applying a statistical significance test to distinguish true effects from random variation.

An illustrative result shows that the old algorithm's advantages in Figure 10.4 were not statistically significant, since the error bars overlap for every plotted point in the interval 10,000–60,000 tuples Consequently, the differences between the methods in this range are inconclusive, indicating that a clear performance improvement cannot be claimed based on this data.

Figure 10.6 highlights a misleading column plot that exaggerates the gap between the old and the new algorithm by starting the y-axis at 0.75 seconds; nonetheless, the results seem significant for all data points above 60,000 tuples.

Overall, the experimental results indicate that the new algorithm provides a clear advantage for datasets with 70,000 to 100,000 tuples, while there is no evident advantage or disadvantage for smaller numbers of tuples.

It is of vital importance, before attempting to draw any conclusions from data obtained through experiments or simulations, that you apply a suitable test for statistical significance Failure to do so means that you run the risk of drawing incorrect conclusions, and – of course – that your examiner rejects the conclusions you propose, since they have not been shown to be significant Which significance test fits your data will depend on the experimental set up and the type of data you have generated, and this is too large an issue to discuss in detail in this book You are therefore strongly advised to consult the statistics literature and to seek the advice of your supervisor on this important issue.

Analyse Your Data

Data analysis involves systematically evaluating collected data against the project’s objectives The clearest method is to assess one objective at a time, determining how well the data satisfy that objective When all objectives are satisfied, the project’s aim is achieved The exact process for judging whether an objective has been met depends on the type of project, and will be described in detail for the different cases.

In a descriptive research project, you typically conduct a survey designed to address clearly stated research questions through defined objectives For example, the aim and objectives of the first project described in Section 5.2.1 were to categorise and compare previous work within a subject area, guiding what data to collect and how to interpret it By assuming that you have already collected, read, and presented the relevant data—such as previous research articles—you establish a foundation for analysis The survey’s objectives translate into specific questions that drive the synthesis of literature, enabling you to map existing findings, identify trends, gaps, and methodological approaches, and contrast how different studies relate to the central topic When done well, this process yields a structured overview of the current state of knowledge, situating your own contribution within the broader scholarly conversation and aligning your results with the research questions you set out to answer.

To achieve the first objective of categorizing prior work, you should systematically analyze the collected data against predefined categories This process requires you to justify how and why each category was developed and why a particular data item is placed in a specific category By documenting the criteria used for category formation, you provide transparent rationale for every assignment The result of this objective is a clear, defensible categorization of the prior literature that organizes studies into meaningful groups and supports subsequent analysis.

For the second objective, you should select a comparison criterion to guide the later analysis of the collected data To fulfill this objective, choose clear, relevant comparison criteria that align with your project goals and the questions you want answered Earlier in the project you may have identified several factors—such as security, performance, usability, and functionality—that will shape these criteria and provide the framework for meaningful data comparison.

10.3 Analyse Your Data 83 could be used as comparison criteria You may even have come across already existing and well-used comparison criteria If you have several potential comparison criteria, you need to justify why you include some of them in your set of criteria, and why you exclude the other ones The result of this objective is your set of comparison criteria, which include different factors that will be evaluated against the collected data.

Objective three involves comparing the previous work against the predefined comparison criteria and evaluating how the collected, categorized data items map to each criterion This analysis reveals the degree to which the literature supports or challenges each factor in the evaluation framework For example, the evidence may show that earlier studies exhibit weaknesses in security issues, while more recent research generally strengthens security-related aspects.

Projects with a strong theoretical focus may extend or compare existing theories or models without practical testing For example, the aim of the first project described in Section 5.2.2 was to extend an existing theory or model, assuming that the relevant data had already been collected, read, and presented According to the first objective, you should identify the details of the extension, which requires analyzing the collected data to determine possible features to include You would then justify why certain features are more relevant than others, and the result is a detailed description of the extension.

The second objective is to introduce the extension to the original theoretical model To fulfill this objective, start by presenting the extension alongside the original model and verify its proper integration into the framework This verification ensures the extension is correctly introduced The outcome is a new theoretical model composed of the original model and its extension, forming a unified theoretical framework.

Under the third objective, compare the original theoretical model with the extended version by evaluating both models against predefined comparison criteria to assess their relative performance The analysis should determine whether the extended model provides improved support for certain features over the original model and identify which features are better supported by the new version.

An applied project typically involves conducting experiments and gaining practical experience from the results In the example project described in Section 5.2.3, the aim was to gain experience from applying a theoretical model to real-world conditions By implementing the model, evaluating its performance, and interpreting the outcomes, the project demonstrates how theoretical concepts translate into actionable insights and informs future work.

In the first objective, you should “set up a simulator for caching of web data”

To achieve this objective, you must design and implement a robust simulator and validate its accuracy by benchmarking it against established baselines Demonstrating the simulator’s validity relative to these baselines is essential, ensuring it is correct and reliable for your evaluation The result is a fully functional simulator that can be employed as the core platform for your future experiments.

To achieve the second objective, you must implement the new algorithm within the simulator and provide a rigorous justification that the implementation accurately reflects the algorithm’s specification This involves integrating the algorithm into the simulation framework, executing representative scenarios, and verifying that the simulator’s outputs align with theoretical expectations The result of this objective is a fully functional implementation of the new algorithm in the simulator, supported by validation evidence from the simulation runs.

In the third objective, you should test and analyze the new algorithm by setting up relevant test scenarios and examining their outcomes For each scenario, explain the result of the experiment and assess what the data reveals Look for patterns across the experiments and consider a categorization of the results to capture common themes The ultimate result of this objective is your clear explanation and categorization of the results obtained from the experiments.

To fulfill the fourth objective, identify refinements to the algorithm's design by examining the results of the third objective This examination reveals where the current design falls short and suggests where improvements can boost accuracy, efficiency, or robustness Based on what was learned in objective three, propose concrete refinements to the algorithm’s structure, parameters, and data flow The recommended refinements should be specific, actionable, and directly tied to the outcomes of the third objective The result of this objective is a clear recommendation for refinements to the algorithm's design, detailing what to change, why, and how those changes will enhance overall performance.

10.3.4 A Comparison of Theory and Practice

As discussed, projects that blend theory and practice contrast theoretical concepts with current organizational practices, revealing gaps and opportunities for improvement In the example project described in Section 5.2.4, the aim was to examine how well the theoretical framework translates into day-to-day operations and to identify where practice diverges from theory The objectives included mapping existing processes to the model, measuring the impact of discrepancies, and proposing concrete recommendations to bridge the gap and strengthen implementation.

“contrast the current theory relating to a particular subject with how companies and organisations support it in practice”.

What is a Good Result?

A good result is one in which a clear conclusion can be drawn with strong confidence The success of a project does not depend on whether the hypothesis is supported or falsified; what matters is whether the data allow a decisive inference In the example project from Sect 10.2, the data can be satisfying and the project successful regardless of whether the experiments show that the new algorithm is better or worse than the old one As long as the data permit you to draw clear conclusions with strong confidence, the work has been performed with success.

Drawing clear and trustworthy conclusions from data begins with carefully designed experiments and the proper application of methods, so the data you collect can be trusted It also requires presenting and analyzing the data thoroughly, without introducing errors that could distort the findings When these steps are in place, the conclusions you reach are reliable and reflect the quality of the work you’ve done.

Be mindful of a potential fallacy—confirmation bias—where the desire for a particular outcome can color how you view data when evaluating algorithm performance After investing significant time to develop, implement, and test a new algorithm, you may hope it outperforms existing approaches so you can present it as the superior solution Your objective is to keep personal expectations out of the data and perform an unbiased algorithm evaluation, presenting the evidence clearly and describing the methodology Rely on sound data analysis and rigorous, transparent testing to compare the algorithms, so the project succeeds regardless of which algorithm turns out to be better.

When a project rests on an explicitly stated hypothesis, you must assess whether the data falsify or support that hypothesis The hypothesis should distill the project’s aim into a concise, testable proposition The entire research effort should be oriented around testing this proposition and judging whether the collected data support or falsify it, so conclusions are drawn directly from evidence.

Drawing your Conclusions and Identifying

In the conclusion, reference should be made to the study’s aim and objectives, ensuring the final findings are traced back to what was set out to achieve If a specific hypothesis guided the work, the conclusion should state whether the hypothesis is still supported by the results of your experiments or investigation; if not, you can propose a revised hypothesis that may serve as the basis for future work If the data do not support the original hypothesis, provide feasible explanations for the discrepancy, and, where appropriate, suggest a revised hypothesis and outline potential directions for future research to test it.

Conclusions should place your results in a wider context; for example, if a relational-data modeling method can express some object-oriented features, consider what that would mean for the field and whether there is a case for reusing existing methods rather than inventing new ones Present this potential conclusion with both supporting arguments and counterarguments, and include a balanced analysis of the project’s strengths and weaknesses by retracing every step of the process and explaining what went right and what went wrong Such an explicit evaluation helps readers identify the key strong points and vulnerabilities of the work, demonstrates your understanding of how a project should be carried out, and, even if mistakes occurred, shows awareness of their impact on the outcome and how they were addressed.

Future directions are a natural next step after any project, since rarely does a study deliver the final answer Outline two kinds of follow-up work: what you would pursue if you continued the project yourself, and what you would propose for other researchers to undertake This approach makes your report more engaging with concrete paths for continuation, situates your work in the broader progress of the field, and helps readers understand how your findings contribute to the larger research landscape Ultimately, you are best positioned to identify and present these directions.

M Berndtsson et al (eds.), Planning and Implementing your Computing Project - with Success! © Springer 2008 to point out future directions, given your experiences from doing the project In the following sections we discuss these issues in more detail.

Summarising the Results

The conclusions chapter is built around the study’s results and provides a concise summary that interprets the findings, places them in context, evaluates them against how the project was carried out, and identifies opportunities to extend the work through future research By summarizing the results, the discussion becomes easier to follow without requiring readers to return to the results chapter.

In order to write this summary, it is useful to think of certain questions, such as:

Key findings in the results chapter show a statistically significant difference in the time it takes users to become proficient with the software when they have access to online documentation versus when they rely on a printed manual The results section presents detailed experimental data and explicitly explains the statistical test used to assess significance In the conclusions chapter, these results are distilled into a concise summary, highlighting that online documentation speeds up learning compared with a printed manual.

Ask yourself which findings you can present with confidence and which remain uncertain The results summary should clearly separate certain findings from less certain ones, because they require different treatment in the subsequent discussion and evaluation For very certain results, outline the potential impact on the field and explain how these findings could be useful in future research or practice For uncertain results, describe the additional work needed—such as new data, methods, or analyses—to achieve greater certainty and to guide future investigations.

Putting the Results into Context

After summarising the results, place them in the broader context of the field to show how the findings relate to existing knowledge and what they mean for theory and practice This section clarifies the impact, significance, and practical usefulness of the work, linking the outcomes to the study’s aims and hypotheses and highlighting implications for researchers and practitioners alike In essence, this is the core part of the report, where the purpose of the research and the meaning of the results are explained in relation to the wider literature and potential applications.

Like the summary of results, you can develop your conclusions by asking a set of targeted questions; this piece outlines typical questions that can guide you in crafting a clear, coherent conclusion that reinforces your findings and brings the article full circle.

“What can my results be used for?” This is perhaps the most interesting point for a reader of your report Some readers may actually be uninterested in your

11.2 Putting the Results into Context 89 report until they read what the results can be used for, and only then will they start looking into what you did exactly, how you arrived at those results, or how your proposed solution to some problem works.

Who can use my findings, and in what ways? For example, if results show that staff using software for computer-supported cooperative work are more productive than those who do not, then it’s essential to discuss the audiences and applications of the result The finding may be most useful for large corporations and less relevant for smaller ones, and if the investigation was limited to staff in a particular industry, you should acknowledge this limitation and offer thoughts on whether the result is transferable to other industries or contexts.

A key question in research is whether you have contributed to advancing understanding in your field, and identifying your contribution with its level of significance helps frame your work In undergraduate projects, the contribution may be modest due to limited time and developing research skills, yet meaningful advances can still emerge It can happen that findings from undergraduate projects are later published as part of research articles.

Consider related research areas that may benefit from your results For example, if your project yields a new algorithm for data mining in image data, that same algorithm could be valuable for data mining in other data types and even for applications beyond data mining; if so, briefly describe the adaptations required for the alternative application Identifying potential spin-offs broadens the audience for your work and illustrates how science progresses when methods developed in one field are modified and transferred to other fields, ultimately being adopted in new domains.

To determine the value of the results, the report should decide whether they offer real-world utility or constitute a theoretical contribution that deepens understanding, and it should justify why they are useful in each case If the results are mainly practical, it should describe concrete ways they could be developed into a theory or lead to modifications or additions to existing theory, such as extracting general principles, formulating new models, or testing predictions across contexts If the results are primarily theoretical, it should discuss concrete pathways by which the insights could translate into practical applications—pilot studies, implementation in relevant workflows, performance benchmarks, and consideration of feasibility, cost, and adaptability Throughout, the discussion should map a trajectory from theory to practice or from practice to theory, outlining the implications, limitations, and concrete next steps.

While parts of the question “How do my results compare with those of others?” are addressed in the analysis section, the conclusions can extend the comparison by citing related work The analysis may contain a detailed comparison with an alternative approach, whereas the conclusions can bring in additional related studies and provide lighter, less detailed comparisons with them, thereby situating your findings within the broader literature and pointing to future research directions.

Assess your findings in relation to the related work: do they align with the existing framework in your field, or do they present something fundamentally new that could overturn current theories? If your results support prior studies, explain how they reinforce the established understanding and what this implies for theory and practice If your findings contradict important related work, provide an extended discussion of possible causes—such as methodological differences, sample variations, or measurement issues—and show how you’ve considered and tested alternative explanations In general, the more controversial your results are, the stronger the arguments and evidence you need to convince readers of their validity.

Evaluating the Process

An evaluation of the work belongs in the conclusions chapter This section highlights the strengths and limitations of the study, helping readers assess the credibility of the results and determine which findings are most trustworthy.

So after having discussed the usefulness of your findings, you must also discuss to what degree they can be trusted.

To strengthen your evaluation, go back to each step of the process to identify weaknesses and mistakes that have become apparent Do this by recalling errors and by reading the report to spot issues as you review For every mistake you identify, assess its potential consequences for subsequent steps and what those consequences mean for the final results For example, when comparing the accuracy of two expert systems that monitor a production process and decide when to stop, you might find you tested five scenarios but omitted three relevant ones; describe which aspects of the systems' performance remain untested and, drawing on what you know about them, suggest how they would likely perform on the missing scenarios This approach helps you create a more comprehensive and credible evaluation report.

In addition to looking for mistakes, you should of course also try to identify the good decisions you made during the process If you produced an excellent experimental design which allows you with high confidence to ensure the correctness and validity of your results, you should point this out in the conclusions In general, you should think about how each good decision you made contributed to the results.

Identifying Future Work

This is an important part of your report, since a good ‘future work’ section not only convinces the examiner that you know how your work fits into the overall development of the subject area, but also helps readers to plan new projects using your work as a starting point In a longer perspective, it will actually be possible to evaluate the significance of your work by looking at the number and quality of projects that have used it as a starting point As with some other sections, it is possible to develop this one by asking yourself a number of questions:

Review your initial objectives and identify any that remain unfulfilled; for each one, specify the actions, resources, and timeline required to achieve completion While it's not desirable to leave many objectives unfinished, you may find one or two that are only partially completed, and these should be treated as the most evident future work, so begin by detailing how you would close the gap—including clear milestones, assigned responsibilities, and measurable criteria to confirm when each objective is fulfilled.

Given extra time for a project, the most important actions are the feasible continuations once current objectives are met, focusing on the future work that meaningfully extends the project’s scope This requires identifying the key factors that influence success and outlining practical next steps that advance the work beyond its present goals A natural extension is to develop a new communication protocol that improves the factors identified as pivotal for success in the study For instance, if the project has isolated the factors critical to enhancing communication in distributed database systems, the next phase would be to design and implement a protocol that delivers improved performance based on those factors.

Do my results reveal open questions that still need addressing? Often, a result will successfully answer the problem and the initial questions, but it will also raise new ones For example, when analyzing biomedical data from experiments that measure activity across thousands of genes using computerized methods, a simple clustering algorithm may perform very well, but the volume of data in each cluster can make interpretation challenging Consequently, future work could focus on developing visualization techniques to help biomedical laboratory staff interpret the clusters produced by the algorithm.

When your results are theoretical, ask what remains to be done before they can be applied in practice Then outline the translation steps from theory to implementation, explaining each phase and providing clear estimates of the effort required at every stage This gives readers a practical roadmap that clarifies what research, validation, resource planning, and testing are needed to move concepts into usable, real-world results.

Presenting and Defending your Work Orally

This chapter helps you in preparing for presenting and defending your work orally, as well as acting as an opponent.

Oral Presentation

What characterises a good oral presentation? One of the main challenges in planning a successful talk is striking the right balance between including enough detail to make the project understandable for the audience and keeping the delivery clear, concise, and engaging Too much technical detail can overwhelm listeners, while too little information may leave important questions unanswered The key is to outline a clear narrative that explains the problem, methods, results, and implications in simple terms, supported by carefully chosen visuals A strong structure—with a compelling opening, logical progression, and a memorable conclusion—paired with thorough practice helps control timing, tone, and pace Focus on the audience’s needs, anticipate questions, and use concise language, concrete examples, and signposting to guide listeners through the presentation Effective planning also means trimming content to fit the allotted time, selecting data that supports the core message, and using visuals to reinforce points rather than distract By balancing detail with clarity and practicing the delivery, the presentation becomes informative, persuasive, and memorable.

To keep a presentation within its allotted time, avoid extraneous detail and ensure efficient time management The key is thorough presentation planning: carefully select the essential details to include, decide what to omit, and map out how each detail will be presented so the audience can grasp the information quickly and clearly.

An effective oral presentation is clear, concise, engaging, and inspiring, and all of these qualities come from thorough planning Careful planning not only ensures you speak with precision but also helps you feel calm and confident during delivery Confidence in the quality of your presentation is the key to persuading and motivating your audience, while academic presentations should focus on communicating a clear message rather than merely entertaining.

Put simply, you can often get by with being boring if your content is well-organized and clearly explained, but you can't compensate for weak messaging by relying on entertainment alone; a strong, well-founded idea must be communicated effectively to make an impact.

For your oral presentation, this book assumes you’ll use slides, whether with a slide projector and transparencies or a computer connected to a video projector Slides are convenient tools that help you communicate your message clearly and emphasize key points to the audience.

Start planning your presentation by deciding your key message and the order in which you will present it Develop a clear structure first, then fill in the details, so you know your main points and their sequence as you shape the content Outlining the structure makes it easier to estimate how many slides you’ll need and how much space you can allocate to each section This structure-driven approach also simplifies discussions with your supervisor, since you can show exactly what you want to say, in what order, and what to leave out or change Keeping the structure in mind helps you anticipate the consequences of any changes and maintain a coherent, persuasive narrative.

Figure 12.1 shows a sample 20-minute presentation structure with questions after the talk The opening two slides introduce the speaker and the talk, with key concepts defined before presenting the aim Two slides cover the background and the arguments supporting the aim, followed by one slide stating the project’s aim and objectives Two slides describe the approach or method used to tackle the problem, and another two slides present the results Note that this plan does not include slides for data collection, which may be unnecessary depending on the project The presentation ends with two slides offering conclusions and suggestions for future work In this model, each slide is assumed to take about two minutes to present, though actual timing will vary with content, so trial presentations are recommended to verify the schedule before delivering the talk.

Fig 12.1 An example presentation structure

With a clear presentation structure in place, begin filling in the details, knowing that the process may require rearranging sections Maintain a living structure document and update it with every change to keep to a fixed number of slides, which helps you present your material cohesively If you skip this discipline, you risk ending up with too many slides and losing the ability to present everything effectively.

Effective slide design hinges on presentation clarity: keep each slide clear, concise, and to the point, avoiding excessive detail that may be informative for the presenter but is often unreadable or time-consuming for the audience; slides should be easily readable and not overloaded with small print, so the message comes across quickly and reliably.

Here is a set of guidelines for preparing slides:

Use a consistent slide layout across all slides to make your presentation easier to understand and keep the audience focused on the content This means placing headings in the same position, using uniform bullets and font sizes, and following a consistent strategy for where to place figures relative to text to achieve strong visual consistency in your presentation design.

Font size is critical: if the font is too small, the audience cannot read the slide, which makes the presentation ineffective; aim for text that is readable by everyone in the room, not just those in the front rows The ideal font size depends on the room size, so go to the presentation venue and test slides with varying font sizes Be aware that many slide tools default to around 32 points, but you can adjust; however, the body text should generally be no smaller than 18 points to preserve readability.

Choose between portrait and landscape orientation based on the slide content, but strive for consistency throughout your presentation, as a uniform orientation improves readability and flow; landscape is normally the preferred orientation for most slides.

Slide language should be concise because slide space is limited Replace long sentences such as “The evaluation of the implemented method shows that it was 22% slower than the current standard method” with a concise version like “Implemented method 22% slower than standard.” Since slides are complemented by spoken explanation, the brief phrases are clarified during the talk Striking the right balance matters: the slide text should be short but not cryptic Focus on clear metrics and direct comparisons, and ensure the spoken narration expands on the abbreviated text For SEO-friendly impact, use succinct, keyword-rich phrases that describe the method and results while keeping visuals clean.

On slides with extensive text, such as quotes, emphasis helps the audience grasp the message quickly When the text is hard to scan, highlight essential words or concepts with boldface or underlining to draw attention to the core ideas and improve comprehension and retention.

Use visualization techniques to help your audience interpret what you are saying, rather than relying on text alone If you include a mathematical formula, visualize the relationship between the variables with a graph; if you have implemented software, use a diagram to show its main components and how they relate; if you describe a process, draw a diagram of the steps and how each step depends on inputs from previous ones In other words, almost any message can be clarified with a visualization At the same time, make sure to spell things out in words—relying too much on figures without proper explanation can make the content vague and hard to grasp By combining clear visuals with descriptive text, you improve interpretation and SEO through keyword-rich, accessible writing.

Acting as Opponent

Next, we outline practical guidelines for acting as an opponent and preparing for opposition; these tips apply equally whether the opponent is a student or the examiner The emphasis is on clear, evidence-based questioning, respectful yet rigorous critique, and well-structured arguments that challenge ideas without personal confrontation By preparing in advance—reviewing the material, identifying potential gaps, and outlining a logical line of inquiry—you can engage effectively, anticipate counterarguments, and contribute to a fair and productive evaluation process.

12.2.1 How to Act as Opponent

During the defense-opposition segment of a presentation, the aim is to subject the presented work to critical examination The opponent asks questions that the presenter must answer, allowing the examiner to judge the presenter’s ability to defend the work and its core arguments Clear, well-reasoned responses demonstrate the robustness of the research, showing that the work can withstand rigorous questioning and scrutiny.

An opponent’s questions should be constructive and aimed at testing the strength of the presenter’s arguments rather than simply criticizing the work Even solid projects can invite negative feedback, but the purpose of questioning is to probe reasoning, not to win by being harsh When a difficult question is asked and the presenter responds with a thoughtful, insightful answer, that outcome marks a successful exchange, showing that the question was well-posed and the answer strengthened the case This approach benefits everyone—the presenter demonstrates the robustness of the work, and the audience gains clearer, broader insights from the discussion.

One common pitfall of acting as the opponent is slipping into a pattern of showing off one’s intelligence and deep understanding rather than asking relevant questions The aim is to test the presenter’s knowledge, so questions should be short, direct, and to the point to minimize wasted time As much time as possible should be devoted to the presenter spelling out and defending the arguments behind the work.

To ask effective questions, you should read the entire report very carefully and prepare a set of questions in advance, with guidance such as the tips in Sect 12.2.2 However, having prepared questions does not mean you should rely on them exclusively; listening attentively to the presentation is essential, because points that were unclear in the report often become clear during the talk, allowing you to drop questions from your list When preparing, bearing this in mind, it’s useful to add more questions than you expect to have time for, so you have backup questions ready to replace those that are answered in the presentation.

These guidelines explain how to scrutinize a report in preparation for the opposition, and they are organized in roughly the same order as most reports, with reference to Chap 14 for the discussion of structure They consist of a set of questions that the opponent can ask while reading, designed to surface strengths and weaknesses in clarity, evidence, methodology, assumptions, and conclusions The list is not exhaustive, but it covers the most important aspects to consider when evaluating a report.

● Has the author explained the problem that is to be investigated in a clear and understandable way?

● Has the author provided convincing arguments for the need to investigate this problem?

● Has the author provided convincing arguments that conducting the investigation will lead to the possibility of obtaining a solution, or increasing our understanding of the problem domain?

● Has the author identified a specific aim to be achieved in the project?

● Is the identified aim explained in a clear and understandable way?

● Has the author derived a list of specific objectives from the aim?

● Are the objectives presented in a clear and structured way?

● Do the objectives support the aim, i.e will fulfilling all the objectives lead to the aim being achieved?

● Has the author clearly identified and explained the methods that could potentially be used in the investigation?

● Has the author provided an insightful discussion of advantages and disadvantages of each potential method for the investigation?

● Has the author clearly stated which method (or methods) was selected for the investigation?

● Has the author provided convincing arguments for the selected method(s)?

● Has the author described clearly how the selected method(s) will be applied?

● Has the author presented the collected data in a clear, understandable, systematic and correct way?

● Is the collected data sufficient, given the stated aims and objectives of the project?

● Has the author made a thorough and systematic analysis of the data obtained?

● Is the analysis described in a clear and understandable way?

● If the data are quantitative, has the author applied significance tests or other numerical evaluation techniques in a relevant and correct way?

● Has the author evaluated the stated aims and objectives in the light of the data obtained?

● Has the author provided conclusions that are relevant, given the stated aims and objectives?

● If the work contains one or more hypotheses, does the author draw conclusions about whether these hypotheses are supported or falsified by the results?

● Has the author provided valid arguments for the stated conclusions?

● Has the author discussed the work in an insightful way, and thereby placed the work into a wider context?

● Has the author identified relevant and plausible continuations of the work?

● Were the objectives of the project fulfilled?

● Was the aim of the project reached?

● Has the project furthered our understanding of the problem investigated?

● Will this work be useful in the future?

● Is the report well structured and understandable?

● Is the report well written?

● Does the author have a critical viewpoint, i.e have sources used in the work been critically evaluated by the author?

● Have terms of importance to the report been clearly defined?

● Is the use of terms and definitions consistent throughout the report?

● Is it clear when something is the author’s own work, and when it is someone else’s work?

● Have all sources used by the author been clearly identified by use of literature references, and have the references been made in a correct way?

● Are you aware of any additional relevant literature on the topic, which has not been used and cited by the author?

● Are you aware of any work which closely resembles the work done in the project, but which has not been identified by the author?

Prepare the Final Version of your Report

Whether you can revise your report after the defence depends on your department’s rules and the outcome of the defence If revision is allowed, you can strengthen the final version by addressing comments from your supervisor, the examiner, and the opponent, and by refining sections you have already identified as needing improvement A practical approach is to compile a list of major potential adjustments and, where possible, decide with your supervisor which changes should be included in the final report and which should not.

A report is a living document: edits you introduce now can affect other sections or chapters, so every change should be evaluated in the context of the whole work Read your entire report as carefully as you would scrutinize an opponent's argument, looking for gaps, inconsistencies, and misalignments in cross‑references This holistic, critical review helps maintain coherence, accuracy, and credibility across the document.

Finally, it is also worthwhile to check the examination criteria one more time.

12.3 Prepare the Final Version of your Report 105

Supplements

Tiêu đề	Thesis Projects A Guide for Students in Computer Science and Information Systems Second Edition
Tác giả	Mikael Berndtsson, Jürgen Hansson, Björn Olsson, Björn Lundell
Trường học	University of Skövde
Chuyên ngành	Computer Science and Information Systems
Thể loại	Thesis project
Năm xuất bản	2008
Thành phố	Sweden

Định dạng
Số trang	165
Dung lượng	2,65 MB