The Model Of Contextualization And Personalization In Learning: The Case Of Var And Mobile Learning...

THE MODEL OF CONTEXTUALIZATION AND PERSONALIZATION IN LEARNING THE CASE OF VAR AND MOBILE LEARNING APPLICATION Nguyen Tung Lam, Ton Quang Cuong (VNU University of Education ) Nguyen Hoa Huy (VNU Center for Education Accreditation ) Pham Thi Hai Yen (VNU ULIS Foreign Languages Special School) Abstract In the digital transformation in education today, learning is increasingly shaped by highly contextualization and personalization concepts Virtual reality and augmented reality technology (VAR) comb[.]

THE CASE OF VAR AND MOBILE LEARNING APPLICATION

Nguyen Tung Lam, Ton Quang Cuong

(VNU University of Education )

Nguyen Hoa Huy

(VNU Center for Education Accreditation )

Pham Thi Hai Yen

(VNU ULIS Foreign Languages Special School)

Abstract: In the digital transformation in education today, learning is increasingly shaped by highly

contextualization and personalization concepts Virtual reality and augmented reality technology (VAR) combined with mobile teaching platforms (Mobile Learning) has been helping to enlarge the learning space, environment, context and opportunities for interactive experiential learning activities toward adaptive learning and the diversity

of learners’ needs Therefore, building up and applying a technology-based model to analyze and generate contextualization and personalization is a critical issue in digital learning Based on the technology integrated into the learning model TPACK-XL, this study analyzed and evaluated the feasibility of the proposed model in contextualization and personalization by applying VAR technology and Mobile Learning toward acceptance, access to learning, and competencies development for today’s learners.

Keywords: contextualization, personalization in learning; VAR; Mobile Learning; Digital Learning;

TPACK-XL

1 INTRODUCTION

In the current digital transformation trend in education in Vietnam, personalized and differentiated teaching and the implementation of the General Education Program are hotly debated topics In particular,

in the context of the increasingly popular and pervasive application of educational technology in all elements and activities of the teaching process, the role of “personalizing learning activities” of learners

is more and more crucial

The issue of personalized teaching was mentioned nearly 40 years ago when B Bloom [2] hypothesized that: all students can learn if they provide and create favorable conditions for them before, during, and after school; Teaching is not about comparing one student to another, but about creating contexts

so that all students can achieve the goals of the pre-established educational and teaching program [3] The premise of personalized teaching was first put forward by B Bloom (1984) in the form of a famous puzzle called “2 Sigma”: How can we teach effectively? If teaching in the style of “One-to-One” (One-to-One) but still ensures cost? In this study, B Bloom proved that, if teaching students in the style

of “tutor” (i.e., “one-to-one”), combined with regular assessment, feedback, and continuous adjustment instruction, the student performed two standard deviations (2 sigma) better than the other students in the

regular classroom In other words, overall, students who receive regular tutoring will do better than the remaining 98% of students in the traditional classroom [4]

When analyzing learning outcomes (evaluated by grades), there is usually a value called the “Normal Distribution” according to the bell curve This value generally creates a stereotype for teachers that there will be some (groups) of students who will not (or are unlikely to) achieve high academic results, in other words, are not capable study However, this is not entirely true because although each student’s learning ability is different, it does not mean that the student cannot achieve the goals set out by the curriculum Because, with different intellectual, cognitive, manipulative, and learning characteristics, each student is a “special individual” in a collective learning community and has his or her own way

of learning to accomplish a common goal The problem for educators is how to “contextualize” the learning process to suit individual learners!

Personalized learning

The idea of personalization of education (or personalized learning) can be traced back to the XIX century when Helen Parkhurst created the Dalton Plan stating that each student can program his or her curriculum in order to meet his or her needs, interests, and abilities; to promote both independence and dependability; to enhance the student’s social skills and sense of responsibility toward others The idea

of customization and personalization of education has evolved ever since In the 1970s, Victor Garcìa Hoz was the first to coin the term personalization in educational science Unfortunately, up to this date, there is no single definition of this concept In order to explain what is usually meant by “personalized learning”, let us have a look at extracts from several documents defining the term at the national or provincial level in different countries

National College for School Leadership, UK: “Personalized learning is a highly structured and responsive approach to learning for each individual child and young person It creates an ethos in which all pupils can progress, achieve and participate It strengthens the link between learning and teaching by engaging pupils and their parents as partners” [7]

The Personalized Learning Foundation, California, USA: “Personalized Learning is a blended approach to learning that combines education delivery both within and beyond the traditional classroom environment The Personalized Learning model fosters a collaborative partnership between the teacher, parent, student and school that designs a tailored learning program for each student according to the needs and interests of each individual student Personalized Learning is truly a 21st-century approach to education that, in practice, through flexibility and choice, honors and recognizes the unique gifts, skills, passions, and attributes of each child, as well as each child’s challenges and obstacles to learning The key attributes that comprise the Personalized Learning model are based upon a solid foundation of the latest educational research findings as to how students learn most successfully These attributes include

a strong emphasis on parental involvement, smaller class sizes, more one-to-one teacher and student interaction, attention to differences in learning styles, student-driven participation in developing the learning process, technology access, varied learning environments, teacher and parent development programs, and choices in curriculum programs No other educational model offered in today’s public education system has integrated these proven educational research results in such an in-depth and comprehensive manner to serve the diverse needs of today’s public education students” [9]

Calgary Board of Education, Alberta, Canada: “Key components of personalization have been identified as integrating and differentiating curriculum, development of learner profiles, flexible program delivery, technology infusion, social construction, and individual student learning plans” [6]

British Columbia Ministry of Education, Canada: “Personalized learning for each student in British Columbia means a shift from delivery of a set of broad, uniform learning outcomes and courses throughout the Pre-K to 12 Education Program, to learning that is increasingly student-initiated, self-directed, and interdisciplinary and that is facilitated by the teacher and co-planned with students, parents and teachers Rigorous learning requirements will continue to be the core of the education program; the amount and nature of required core learning will change as students’ progress through the program… Personalized learning provides individual learners with the differentiated instruction and support they need to gain the required knowledge, skills and competencies and also provides them with the flexibility and choice they need to develop their individual interests and passions” [5]

Personalized learning, broadly understood as Adaptive Learning, is the formation of the teaching process in the direction of teaching “learners” rather than teaching in “classes” focusing on students’ needs and establish individual learning paths for learners Therefore, learning increasingly bears the personal imprint of the learner in a bolder way Educators have introduced several concepts such as differentiation teaching, individualization, personalization, and customization or tailored to indicate the trend of teaching that needs to be focused, towards learners, for the learners themselves The essential components of Adaptive Learning are:

- Differentiated learning is a “prism” to detect, screen, classify and acknowledge learners as

different (attributes, qualities, abilities of each individual) in the relationship “together towards unity

in diversity” (the purpose of teaching is unified but must be realized based on the differences of learners);

- Individualized learning acknowledges the “individual” uniqueness of the individual of the learner

in the learning process, in relation to the learner himself (uniqueness, individuality) For example, the choice of disciplines, subjects, and technological means according to individual learners’ needs and interests; programs are designed to be diverse, flexible, interconnected, highly selective to meet the individual needs of learners, learners are unique (customized learning); the program is assembled, designed and customized for themselves (tailored learning);

- Personalized learning classifies the “separate” of learners in relation to other individuals in the

classroom, collective, or learning community For example, learners have different learning styles, so teachers will have to diversify teaching methods to maintain a fair opportunity in accessing learning… Although the names and manifestations of these teaching approaches may be different in practice, they all aim for a common goal: learning must come from the learners’ own context, there is no general concept of learning (one-size-fits-all) or learning by itself, which must be specifically recognized from

a combination of attributes and abilities of learners

Contextualized learning

Based on a constructivism theory contextual learning transforms information presentation (the task

of teacher) to developing some skills, providing opportunities for students to construct meaning based

on their own experiences during the learning process (i.e., makes learning relevant, increases learner confidence & enthusiasm, and enhances interest in long-term goals)

Following this trend, the current teaching process, which is directly oriented towards the learners, has transformed into the following branches: - formal teaching according to established programs (including face-to-face and online teaching); - individual-oriented teaching (contents and formats that meet individual needs, driven by individual capacity, speed, interests ); - group-oriented teaching

within a particular organizational setting (e.g., a classroom, in a school ) and network group (meeting the needs of the network group outside the organization); - incidental teaching (learning what, from whom, at what time according to the needs of “randomly, by chance”)

On the other hand, with the development and strong support of educational technology, personalized teaching gradually becomes a new “form” of social relations in teaching All participants contribute to creating knowledge, contributing a separate “Me” that has been customized according to the context and personal experience in the process of working towards the common goal

In essence, this new form of relationship is reshaping a new approach to teaching: the trend

of personal experience based on knowledge sharing (with purpose) and multi-dimensional social interaction in learning-based teaching In other words, teaching is about providing and supporting flexible, customized learning experiences that meet an individual’s unique needs through feedback, pathways, personalized learning progress, and timely resources (rather than providing common learning experiences for all classes)

Personalized and contextualized learning with VAR support

To develop high-level thinking, learners not only observe and remember things and phenomena but also interact to discover them However, in reality, it is not always, anywhere and with any audience that learner can do this experiential activity VAR/MR (mixed reality) solutions applied in teaching will create opportunities for personal interaction in physical/virtual space, multi-dimensionality, increasing accessibility and information processing; expanding space, learning environment (real-virtual); develop creative thinking and problem-solving abilities; enhance adaptive learning, immersive learning and seamless learning for individual learners

Besides the ability to integrate multimedia and multi-format, VAR/MR aims to create a highly simulated environment, contextualize the problem, and activate multi-dimensional interaction in teaching VAR/MR solutions force individual learners to face problems/situations (simulated in virtual space), self-identify and make appropriate choices and solutions, creatively solve them problems on their own in hypothetical situations Thereby, learners simultaneously experience the psychological states that appear when performing their own decision-making tasks By repeating the actions and experiences integrated with VAR/MR technology over and over again, learners will perform learning tasks to achieve their teaching goals in their own way Specifically: - Remove barriers in content access: learners interact directly with learning content, extract virtual objects from the display screen (simulation, 360o videos,

360o photos, audio ), self-actualization performs object navigation tasks - Activate multi-sensory activities, multi-channels of movement: hearing, vision, and movement are integrated into experience activities and emotional and psychological states - Promote creativity and critical thinking: the virtual environment will create a sense of security from within, encourage risk-taking, failure, learning from mistakes, and thinking clearly to find a solution Some studies also show that learners who interact with lifelike VAR/MR content tend to ask higher-order questions to dig deeper into abstract concepts - Develop independence, confidence in learning: virtual learning context allows learners not to be afraid of making mistakes (freely making mistakes from which to learn from mistakes), promoting independence, self-esteem, and confidence in learning, forming a belief in being ready to overcome challenges to achieve goals - Encourage individual efforts in collaborative activities: real-life situations and problems

in project teaching are contextualized thanks to VAR/MR technology, learners are “embedded” in a virtual, online environment continues to interact with other learners to accomplish common goals

Mobile technology enabled for personalized and contextualized learning

Using technology devices and mobile and approaching BYOD (Bring Your Own Devices) is a popular trend today in education around the world) Personalizing learners also means allowing the use and enhancement of personal applications and devices according to user needs Through the mobilization and exploitation of personal technology devices for learning, teachers and learners can speed up accessing and processing information thanks to educational applications running on the device platform, mobile devices (Mobile Apps); increase interaction, sharing and connection of individual learners These applications are designed and developed on mobile platforms (App, Web) of smartphones, tablets, laptops, and other portable and wearable devices for an authentic learning experience in a personalized environment

Learning contextualization can be personalized at high levels of both learners’ and teachers’ activities App-users should adapt to technological, metacognitive, academic skills in social-share networks in cyber-physical environments

On the other hand, in personalized teaching, the use of iPad, tablets, laptops, smart connected devices, mobile and handheld (boards, smart teaching devices, etc.) using cloud computing platforms, web infrastructure, connecting to large databases, easily sharing and interacting in learning; convenient

in centralizing, storing and distributing digital learning materials in multiple formats (simulation, 3D ); replace traditional teaching tools and equipment (boards, books, printed materials, visual teaching aids, etc.) These personal devices can also be exploited (with control and management) as a tool to connect, share information, interact and communicate instantly between stakeholders in the field of education Moreover, the mobile context has a close relationship with the learning environment with mobile and ubiquitous learning strategies (i.e., blended, flipped, and personalized learning approaches) Gamification, VAR/MR, simulation, 3D environment, digital resources will motivate learners to interact actively and perform multiple academic, metacognitive, and technology competencies The lecturing, narrations, presentations, discussions, debate case studies, assessment, and reflection, etc via mobile devices encourage learners to transform from the followers - customers to producers - creators at their convenience, flexibility, and portability The well-organized and structured mobile context also indicates one’s personality, characteristics of individual learners’ progression, control, mastering, and development throughout the learning process

Shifting TPACK-XL to PLM model

TPACK (Technology, Pedagogy and Content Knowledge) is a well-known exploratory theory that

integrates technology It provides a framework of three specific areas related to the nature of knowledge

in the learning environment In the TPACK model, there is a dynamic concept of the learning process, working together to address the teacher’s concepts of knowledge and pedagogy, supporting matching techniques and entering the subject’s content (Mishra, Koehle, 1996, 2005)

TPACK-XL: Saad and others (2012) created a transformative view of integrated technology

in general mode, adding components such as Specific Learners (L) and Language-specific scene (X)

in the learning process According to this model, educational technology (T) considers the essential role in learning (i.e., platforms, solutions and tools for interaction, content sharing, learning resources, assessment, etc.) between teachers and students in the online environment) Figure T must be connected

to other components in the TPACK-XL model (in short, TPCLX)

Figure 1 Educational technology interconnected constructive model of TPCLX

(T.Q Cuong et all, 2020)

PLM (Personalized Learning Model)

The model PLM has been proposed by Karampiperis & Sampson (2005) [8] and Nguyen Viet Anh (2009) [1] The personalized learning model PLM proposed reflection on TPCLX with learning management system (LMS) framework and relationship within core constituents

Figure 2: Model of relationships in adaptive teaching Users’ module: We have proposed to use the model using probabilistic values to quantify the

knowledge level of learners, monitor and summarize data on student progress and convey it to the instructor The model also provides learner feedbacks and suggestions

Module for managing the teaching process: We have proposed to add a set of tasks to model the

course content including a set of concepts and tasks The tasks are the basis for the adaptive system to give instructions to each learner on how to complete the task Modeling course content through tasks

to solve classes of courses whose content not only provides pure concepts but also requires learners to apply them to complete assignments

Module adapting functions: We build the learning process to meet many goals and needs of each

learner at the same time instead of meeting each goal separately Our model, in addition to giving concepts learners need to learn, also suggests steps on how to complete tasks that learners have not completed

Support module and technical infrastructure: contains content, learning resources, support

software and information-related description (metadata) Learning resources such as lectures, tests, examples, and exercises are often stored as hypertext and hypermedia as HTML files

Table 1 Specifications of VAR/ML support for PLM modules

Model

Component VAR/ML support integration

TPCL-LEARNING

MODULE

(including PL, CL)

Apps focusing on:

- immersive learning and seamless learning

- individual knowledge

- high-level cognitive objectives

- knowledge construction of community collaboration; and

- diversity of learning methods

- learner sensitivity

TCXL - USER

MODULE

(including CL, XL)

Apps providing:

- context sensitivity (spatial context switch)

- deliver on-demand resources

- seamless connection or automatic

- personal independent selection resources

TPXL -MEDIA

SPACE

MODULE

(including PL, XL)

Apps integrating:

- Contextualized learning space, personalized experience

- instant reflection, feedback, and task support

- tools with all-in-one functions, systematized and specialized tools

- learners judge the technology, the learning scenarios and environment

- automatically sensing technology

- adaptive evaluation of learning; personalized learning

TPCXL

(ADAPTIVE

MODULE)

Apps engaging:

- Adapt to individual needs, conditions, learning facilities of the traditional

classroom

- automatically sensing technology environment

- combination with the mobile interconnected real community to communicate anytime and anywhere

- learning scenarios are automatically, personally recognized

METHODOLOGY

VAR and ML solutions must be designed and applied towards supporting the mechanism that explores and develops the learning capacity of learners by utilizing mobile apps in the personalized learning process with collaboration in real context

The Student EdTech (VAR/MR and ML) perception, acceptance survey has been designed with 19-item Likert-type Participants rated their agreement with each item on a 5-point Likert-type scale (5

= Totally agree, 4 = Agree, 3 = Fairly agree, 2 = Confused, 1 = Disagree) The measure of PLM domains used in this study represents participants’ evaluations of the use of VAR and ML in a number of learning activities based on TPCLX model constituents The assessment sample is evenly distributed among students (N=144) from Educational Technology Management, Teacher Chemistry Education and ICT, Computer Science

The PLM model focuses on general consisting of modules using themes, content, apps or platforms

in mobile learning with VAR and has been used in this study because of its generic nature since the sample consists of students of various areas of personalized and contextualized education attending different study programs and courses The original PLM inventory was written in English with Vietnamese translation and was proof-read

Data analysis and discussion

All 16 out of 19 statements were basically agreed by the respondents (average rating from 3,410

to 3,944) The research team also divided the respondents (N=144) into two groups by age: from

18-20 years old and over 18-20 years old, these two groups correspond to two age groups with different experiences, tools and learning styles

Evaluation of PLM Teaching Support Technology (item SP.1 to SP.4): At least 72.2% of

respondents agree and completely agree with the statements given:

(SP1) New technologies in education now support personalized teaching (Mean = 3.94; Std Deviation

= 0.800); (SP2) Technology to increase interactivity and connection in personalized teaching (Mean = 3.94; Std Deviation = 0.826); (SP3) Technology to support real-virtual space, supporting learning according to individual learners’ plans, needs and abilities (Mean = 3.85; Std Deviation = 0.895); and (SP4) Technology that allows learners to learn in their own way (Mean = 3.85; Std Deviation = 0.831)

With Sig value = 0.039 < 0.05, the results show that there is a statistically significant difference between the two groups of respondents for the statements SP1 (average difference: 0.248) and SP2 (average difference: 0.305) Subjects aged 18-20 years had higher rates of agreeing and completely agreeing (SP1: 81.0%; SP2: 86.9%) for these statements than subjects aged 20 years and older (SP1: 81.0%; SP2: 86.9%) SP1: 63.3%; SP2: 66.7%)

Evaluation of the usefulness of VAR, ML (item PU1 to PU5): The percentage of respondents

agreeing and completely agreeing with the statements made for these contents is high, from 79.2% – 91.7%, specifically: (PU1) Using VAR, ML improved self-study efficiency – 87.5%; (PU2) Respondents improved their experimental practice using VAR, ML – 79.2%; (PU3) Using VAR, ML helps connect subject knowledge with life – 87.5%; (PU4) Respondents feel that using VAR, ML is very helpful in experiential learning – 91.7%; and (PU5) the use of VAR, ML helps respondents improve their ability

to interact with teachers – 84.7%

With Sig value = 0.009 < 0.05, the results show that there is a statistically significant difference between the two groups of respondents for the assessment of PU3 (average difference of evaluation: 0.407) and PU4 (difference of assessment mean: 0.407) price: 0.445) The group of subjects aged

18-20 years had a higher percentage of agreeing and completely agreeing with these statements (PU3: 44.0%; PU4: 53.6%) than the group of subjects aged 20 years and older (PU3: 44.0%; PU4: 53.6%) PU3: 26.7%; PU4: 35.0%) Besides, the percentage of people who answered Confused about these two statements of both groups is high, over 40%

Evaluation of use assessment of VAR, ML (item PEOU1 to PEOU5): the percentage of respondents

agreeing and completely agreeing with the statements made for these contents is on average, from 48.3

% – 51.7%, specifically: (PEOU1) I use VAR, flexible ML to study anytime – 50.0%; (PEOU2) I easily handle problems that arise when using VAR, ML – 48.3%; (PEOU3) I feel that the operation using VAR, ML is very simple – 48.3%; (PEOU4) I feel VAR, ML is very easy to use – 48.3%; and (PEOU5)

I interact easily with learning content using VAR, ML – 51.7% Besides, the percentage of people who answered Confused about these two statements of both groups is high, over 31.7%

Evaluation of Intent, ability to use next VAR, ML (item PL1 to PL5):

With Sig value = 0.001 < 0.05, the results show that there is a statistically significant difference between the two groups of respondents for the statements PL2 (interval mean difference: 0.395) and PL4 (average difference: 0.407) Subjects aged 18-20 years had higher rates of agreeing and completely agreeing (PL2: 75.0%; PL4: 77.4%) for these statements than subjects aged 20 and older (PL2: 75.0%; PL4: 77.4%) PL2: 48.3%; PL4: 50.0%)

Table 2 Descriptive Statistics of students

N Mean Deviation Std Varian- ce Difference Mean

Confidence Interval of the Difference

Lower Upper

SP1 New technologies in education now support well

personalized/mobile teaching. 144 3.944 .800 .640 .248 -.025 .520 SP2 Supportive technology increases interactivity and

connection in personalized/mobile teaching. 144 3.944 .826 .682 .305 .018 .591 SP3 Technology to support real-virtual space, support learning

according to the individual learner’s plan, needs and abilities. 144 3.847 .895 .802 .167 -.144 .477 SP4 Technology allows learners to learn in their own way 144 3.847 831 690 138 -.148 424

PU1 Using VAR, ML has effectively improved my self-study 144 3.486 1.090 1.189 -.024 -.392 344

PU2 I have improved my experimental practice using VAR, ML 144 3.410 1.179 1.390 017 -.379 412

PU3 Using VAR, ML helps me connect subject knowledge

PU4 I feel using VAR, ML is very useful in experiential learning 144 3.826 1.124 1.263 445 076 815

PU5 Using VAR, ML helps me improve my interaction with

PEOU1 I use VAR, flexible ML to study at any time 144 3.424 979 959 -.131 -.459 197

PEOU2 I easily handle problems that arise when using

PEOU3 I feel the operation using VAR, ML is very simple 144 3.354 935 874 -.136 -.460 189

PEOU4 I feel VAR, ML is very easy to use 144 3.292 1.016 1.033 -.186 -.538 167

PEOU5 I interact easily with learning content using VAR, ML 144 3.521 900 811 -.021 -.335 293

PL1 I am ready to continue exploiting VAR, ML tools for

personal learning. 144 3.708 .860 .740 .043 -.258 .344 PL2 I am ready to share my experience of using VAR, ML

PL3 I believe that the use of VAR, MR will support the

development of individual learning capacity. 144 3.819 .858 .736 .262 -.039 .563 PL4 I believe that using VAR, MR will help develop other

technology skills. 144 3.854 .869 .755 .407 .108 .706 PL5 I believe that the use of VAR, MR will continue to

support motivation and enjoy in learning. 144 3.833 .861 .741 .286 -.012 .583

Table 3 The main score of students’ statistics

To be able to effectively implement VAR/ML in learning context (i.e., smart environment) for both teachers and learners, it is necessary to synchronize the relationships and conditions towards the learning outcomes, objectives and policies The relationship expressed between entities operating on the psychological, academic and technological level (teachers, learners, managers, administrators, service providers, etc.), compatibility and response of various PLM modules (tools for content construction and development, learning activities and analytics, instructional management, assessment and feedback, personalization and collaboration, etc.), and the adaptive principles

Furthermore, at the micro level (teaching and learning segments), the following discussion may

be raised to acknowledge the presence of easiness and usefulness of friendly learning tools (VAR/ML) that make the PLM model more popular and adaptable for different adaptive learning modes It is also important to suggest the framework, standards, or indicators of quality and effectiveness for apps using different education levels, mobile and virtual learning formations, and social network of participants Last but not least, this conception also relates closely to how better understanding the learners’ behaviors (flexibility, mobility, adaptability, etc.), the challenges of conducting smart, seamless and immersive, technology-enriched learning activities

In sum, a smart learning environment that is contextualized with technology must be designed

in a way that supports the discovery and development of learners’ learning capabilities using mobile applications virtual reality technology in the learning process with personalization

CONCLUSION

The smart learning environment is both a characteristic and an indispensable result of digital transformation in education The conception of smart learning proposed widely using VAR/ML toward personalization in learning process

Based on TPCLX framework the MPL model presents a new perspective on digital transformation

in education through the adaptive solutions and VAR-Mobile personalization (mobile tools, content, context with VAR environment etc.)