1. An Intelligent System for Academic Advising Authors: Oscar Lin （林复华） Frank Zhang Dunwei Wen Athabasca University Canada 第十届全球华人计算机教育应用会议 GCCCE2006, 中国 北京 清华大学

2. Needs in Distance Education The academic advisors are handling more and more questions by email or phones then ever before. The means of communicating and dispersing information are vital in serving and retaining students. Providing personalized, student-oriented help, is particularly important to help students fight against isolation.

3. Academic Advising in Distance Education The academic advisors need to be more flexible, and adaptable. Table 1. Typical Job Objectives of MSIS Graduates (MSIS 2000) Advancement in current jobOutsourcer/systems integrator First or middle IS managementProject manager Management consultantSystems analyst/designer Internal consultant/senior staffTechnical specialist CIOIT Liaison Business analystA Ph.D. program leading to teaching EntrepreneurElectronic commerce specialist

4. The Complete MSIS Curriculum

5. Literature Review Intelligent systems for advising –Since 1980s –Knowledge-based systems –Run on mainframes environments or standalone PCs User Interface Knowledge-based Advising System Database KB Original standalone advising system

6. Challenges Goal: –Making the service more flexible and automated –Propose a methodology of developing e-Scheduler which can be applied to e-Business, e-Education, e- Commerce. Strategies: –Realize the interoperability with other systems in educational environments –Provide knowledge modeling and management methodology and tools

7. Planning Agent Delegate Monitoring Agent Inform Interface Agent Notification Agent Evaluation Agent Inform Delegate Inform Multi-agent System based Architecture for E-Advisor (Flexible, Reliable, Reusable)

8. Planning Agent Delegate Monitoring Agent Inform Interface Agent MSc IS Ontology (agent) Notification Agent Evaluation Agent Monitor Inform Delegate Inform Multi-agent System based Architecture for E-Advisor (Flexible, Reliable, Reusable)

9. Planning Agent Delegate Learner Profile Monitoring Agent Inform Course DB Monitor Update Interface Agent MSc IS Ontology (agent) Notification Agent KB Evaluation Agent Monitor Inform Delegate Inform Multi-agent System based Architecture for E-Advisor (Flexible, Reliable, Reusable)

10. Planning Agent Delegate Learner Profile Monitoring Agent Learner Inform Course DB Notify Monitor Update Interface Agent MSc IS Ontology (agent) Notification Agent KB Evaluation Agent Monitor Inform Delegate Inform Multi-agent System based Architecture for E-Advisor (Flexible, Reliable, Reusable)

11. Planning Agent Delegate Learner Profile Monitoring Agent AdvisorLearner Inform Course DB GUI Notify Monitor Maintain Update Interface Agent MSc IS Ontology (agent) Notification Agent KB Evaluation Agent Monitor Inform Delegate Inform Multi-agent System based Architecture for E-Advisor (Flexible, Reliable, Reusable)

12. Planning Agent Delegate Learner Profile Monitoring Agent GUI AdvisorLearner Inform Course DB GUI Administrator Notify Monitor Maintain Update Interface Agent MSc IS Ontology (agent) Notification Agent KB Evaluation Agent Maintain Monitor Inform Delegate Inform Multi-agent System based Architecture for E-Advisor (Flexible, Reliable, Reusable)

13. Knowledge Modeling and Representation Protégé OWL --- Domain knowledge model Program structure and regulations Decision Tables --- Academic advising knowledge Petri Net based course pre- requisite model (Lin, et al., 2005) Preference-based Optimization Model

14. The overall program planning workflow controlled by the interface agent of a student Enter the program Semester # = 1 Opportunistic Planning Pre-planning Final decision Semester # increases by 1 Credit requirement Satisfied? Graduate The course schedule for the next semester is unavailable yet The course schedule for the next semester is available Deadline for course registration has passed.

15. Planning Requirements and Preferences Plan parameters –expected graduation semester –numbers of courses to take for the remaining semesters –designated courses to take, etc. Preferences –job objectives –career tracks –specialization –assessment style –route (project or essay)

16. Overall Objective Function of Planning Agent The quality of goodness --- a weighted sum of the following two objectives –Minimum span-time –Maximum the degree of preference fitness

17. Search Tree and Plans Now 1 st semester to come 2 nd semester to come B1B2Bm … 3 rd semester to come …

18. Ontology-Based MSc IS Course Pre-requisite Determination Pre-requisite topic set 1 Course A Course C1 All Pre-requisites Topic Set Pre-T(A) Ontology-based Reasoning A set of courses C = {C1, C2, …, Cm} whose topics minimally cover the topic set T. match Goals: 1.Help professors to determine the pre-requisites of a course 2.Pre-requisite maintenance: if a course is added or deleted or revised, the pre-requisite relationship may be changed. 3.Help students to know what courses need to be taken first if he/she wants to take a course. Learning Object/Unit 1 LO2LO3 PTS2PTS3 Course C2 Topics T(C1)Topics T(C2) 1. Pre-T(A)  T(C1)  T(C2)  …  T(Cm) 2. If  B = {B1, B2, …, Bn}  C, and Pre-T(A)  T(B1)  T(B2)  …  T(Bn)  T(C1)  T(C2)  …  T(Cm)  T(B1)  T(B2)  …  T(Bn) MSc IS Ontology

19. Theoretical and Empirical Results Test with student data

20. Strengths and Weakness in Practice Adapt to preference changes Reusability: domain knowledge

21. The Current Project Team Principal Investigator

22. An Ideal Project Team

23. Conclusions The e-Advisor has been developed to add many benefits to both staff and students –40 AU MSc IS students are using e-Advisor –Active role of students in planning their program study suited to their goals and desires –Lessen the workload on human advisors –Allow the administrators to better design programs and work out schedules based on students’ needs and status. MAS approach facilitates software development, maintenance and upgrades MAS architecture fosters collaboration in system design, providing the all members of the team can understand and contribute to the design.

24. Future Work More extensive testing Data mining to add intelligence to the agents Ontology maintenance The first generation system has provided us experience in designing more generalized tools that could be applied in many academic programs and schools Web services development and integration into MAS.

25. www.e-advisor.org Thank You!