Lecture Notes in Computer Science- P63 ppt

courseware recommendation agent is in charge of recommending a personalized learning courseware to learner according to learner’s ability and courseware’s diffi-culty.. The learner inter

courseware recommendation agent is in charge of recommending a personalized learning courseware to learner according to learner’s ability and courseware’s diffi-culty The system architecture is shown as Fig 1

2.2 Interface Design

The interface of the system includes two parts : learner interface and teacher\expert interface, which are managed by learner interface agent and teacher interface agent The learner interface agent provides a flexible learning interface to interact with learners, conveys the learners’ feedback information and testing results to the diagno-sis/assessment agent, receives the recommendation coursewares from the adaptive navigation agent,and then, displays the coursewares to the learners Through the learner interface agent, learners can choose interesting course categories and units to study and use on-line helping to solve the encountered problems during the learning process Learners can also enter appropriate keywords for searching the needed courseware through the system’s search mechanism during learning process If a learner visits the personalized learning system for the first time, he/she must register

as a legal user by inputting his/her individual basic information, and then the learner interface agent stores these individual basic information to learner account database through the database management agent

Teacher/Expert interface agent provides a friendly interface[15] to interact with teachers or experts Through teacher interface agent, teachers or experts can up-load, delete, or revise courseware and testing items stored in the courseware reposi-tory and testing items database Teachers can also manager the answer document stored in answer document repository, give training cases to train the auto-reply agent for automatically answering students’ questions

2.3 Personalized Web-Based Tutoring

The personalized web-based tutoring module includes three agents: diagno-sis/assessment agent, adaptive navigation agent and courseware recommendation agent The three agents through a standard protocol, collaborate with each other to achieve personalized tutoring After a beginner logs into, the diagnosis/assessment agent will give a questionary for collecting learner’s profile information(learner’s behaviors, interests, cognitive characteristics, knowledge level and ability) and store these profile information to learner profile database for providing personalized tutor-ing services, and then conveys learner’s profile information to courseware recom-mendation agent and adaptive navigation agent The courseware recomrecom-mendation agent based on learner’s profile information estimates learner ability, and then selects suitable difficulty levels courseware for learner[12] Based on the learner’s profile information and coursewares recommended by courseware recommendation agent, the adaptive navigation agent conduct personalized curriculum sequencing for learner[9], meanwhile, communicates with the learner interface agent to guide the learning contents according to the planned learning path for individual learner and the learning processes of individual learner are also recorded into the learner profile data-base for personalized tutoring After learner finishes the entire courseware planed by

the personalized tutoring system module, the adaptive navigation agent will notice the diagnosis/assessment agent to randomly generate a testing sheet to the learner for per-forming a post-test in order to evaluate learning performance The generated testing sheet in a post-test will be transformed to learner interface agent, and then displayed

to the learner The post-test results are also provided to the learner for self-examination and stored into the learner profile database So far, the learner finishes the entire learning process for a learning course unit

2.4 On-Line Helping

If learners encounter problems during the learning process, their learning perform-ances could be significantly devastated due to no instant aid So, online helping sys-tem is very important for an adaptive e-learning syssys-tem[10].In this multi-agent framework, the on-line aid function is undertaken by auto-reply agent The auto-reply agent can automatically reply most of the questions submitted by the students with the answers provided by the teachers If no feasible answer[11] can be found in the an-swer document repository, the agent will forward the questions to the teacher/expert interface agent, and then the auto-reply agent will remind and assist teacher in an-swering the question Once the new answer is available, the system will send it to the learner via the learner interface agent Moreover, the teachers can review all of the questions submitted by the learners and the answers replied by the systems with cor-responding satisfaction degrees rated by the learners, which is helpful to the teacher in realizing the learning status of each learner and the performance of the system

2.5 Courseware/Testing Items Management

The courseware/testing items management agent administers the details of maintain-ing the courseware repository and testmaintain-ing items database The agent provides lots of robust functions for teachers to upload, delete, or revise the content of courseware in the courseware repository Through the agent, experts can design testing items for learning content Because all coursewares in the courseware repository have followed the standard of SCORM 1.2 (Sharable Content Object Reference Model) metadata information model (Advanced Distributed Learning)[14],the agent can exchange courseware with other e-learning systems

3 Experiment and Evaluation

Based on the multi-agent framework, an adaptive e-learning system has been im-plemented The proposed system is implemented on the platform of J2EE More-over, the genetic algorithm, data mining algorithm and machine learning are used to implement this system Fig.2 is one of the learner’s interface To verify the sys-tem’s effectiveness for the proposed personalized intelligent learning system, some high school students were invited to test this system To evaluate learners’ satisfac-tion degree for the proposed personalized e-learning system, a quessatisfac-tionnaire which involves many questions distinguished six various question types(table1) were

Table 1 The six question types

Question type Description

The satisfy of

system services

To investigate whether learners satisfy the provided learner interface and course materials

Learning interests To investigate whether learners are interested in using the

proposed adaptive e-learning system for mathematical learning

learning mode To investigate whether learners can accept the proposed

learning mode with personalized tutoring learning interction

between teachers

and learners

To investigate whether the proposed adaptive e-learning system affects learning interaction between teachers and learners

learning attitude To investigate whether learners with computer use the

proposed personalized e-learning system for learning at home

learning

performance

To investigate whether the proposed personalized e-learning system can promote learners’ learning performances and confidence

Fig 2 The learner’s interface

designed to measure whether the propose.There are totally 216 effective question-naires filled out by learners who participated in this experiment Among 216 effec-tive questionnaires, 78% learners selected “strongly agreed” or “agreed” items,13% learners selected “neutrality” items, only 9% learners selected “strongly disagreed”

or “disagreed” items The investigation result illustrates that the multi-agent adap-tive e-learning framework is high feasible and robustd e-learning system satisfied the real requirements of most learners

4 Conclusions

This paper proposed a multi-agent framework for building adaptive e-learning system The proposed architecture considered all indispensable functions which include diagno-sis(assessment),online-helping, adaptive navigation and courseware recommendation, and so on, in the personalized e-learning system This paper makes a critical contribu-tion: proposed a multi-Agent framework to realize an adaptive e-learning system The experiment also demonstrated that the system can efficiently and splendidly perform personalized web-based tutoring works However, the project is still in its early stages; there are still a lot of works left to be done and there are still many open design and im-plementation issues Additionally, in order to improve the system in terms of functional-ity and efficiency, some design aspects need further investigation

Acknowledgment

This material is based upon work funded by Zhejiang Provincial Natural Science Foundation of China under Grant No.Y107750 Thanks to the financial support from Natural Science Foundation of China with granted number 60773197

References

1 Lu, S.: The research and application of multi-agent technology in the Network educa-tion[D] Nanjing University of Technology (2004)

2 Qin, Y.: Concept of agent and its application in network teaching environment[J] FuJian Computer 8, 29–30 (2003)

3 Ming, Z.: The research of intelligent distance teaching system based on agent[D] School

of foreign Languages central China Normal University (2007)

4 Wooldridge, M.J., Jennings, N.R.: Software engineering with agents: pitfalls and prat-falls[J] IEEE Internet Computing 3(3), 20–27 (1999)

5 Bellifemine, F., Poggi, A., Rimassa, G.: Developing multi agent systems with a FIPA-compliant agent framework[J] Software - Practice &Experience 31, 103–128 (2001)

6 Changjie, T., Lau, R.W.H., Qing, L., Huabei, Y., Tong, L., Kilis, D.: Personalized course-ware construction based on web data mining[J] In: Proceedings of the first IEEE interna-tional conference on web information systems engineering, vol 2, pp 204–211 (2000)

7 Chen, C.-M., Duh, L.-J.: Personalized web-based tutoring system based on fuzzy item re-sponse theory[J] Expert Systems with Applications, 1–18 (2007)

8 Chen, C.-M.: Intelligent web-based learning system with personalized learning path guid-ance[J] Computers & Education, 1–28 (2007)

9 Chen, C.-M., Liu, C.-Y., Chang, M.-H.: Personalized curriculum sequencing using modi-fied item response theory for web-based instruction[J] Expert Systems with Applica-tions 30(2), 378–396 (2006)

10 Hwang, G.-J., Yin, P.-Y., Wang, T.-T.: An enhanced genetic approach to optimizing auto-reply accuracy of an e-learning system[J] Computers & Education, 1–17 (2007)

11 Tseng, J.C.R., Hwang, G.J.: Development of an automatic customer service system on the internet Electronic commerce research and applications, 9, doi:10.1016/j.elerap.2006,04.009

12 Tseng, J.C.R., Hwang, G.J.: A novel approach to diagnosing student learning problems in e-learning environments WSEAS Transactions on Information Science and Applica-tions 1(5), 1295–1300 (2004)

13 Lind, J.: Issues in agent-oriented software engineering In: First international workshop on agent-oriented software engineering (AOSE 2000), Limerick, Ireland (2000)

14 Huang, Y.-M., Chen, J.-N., Huang, T.-C.: Standardized course generation process using Dynamic Fuzzy Petri Nets[J] Expert Systems with Applications 34, 72–86 (2008)

15 Zhao, J., Zhu, X.: The Technology of Visual C++ Programming Based on Microsoft Agent COM[J] Microelectronics & Computer 10, 59–62 (2002)