1. Auto Diagnosing: An Intelligent Assessment System Based on Bayesian Networks IEEE 2007 Frontiers In Education Conference- Global Engineering : Knowledge Without Borders, Opportunities Without Passports Liang Zhang, Yue-ting Zhuang, Zhen-ming Yuan, Guo-hua Zhan

2. Outline  Introduction  Architecture of the system Authoring module Training/test module Monitor module Grading module  Knowledge map  Diagnosing learning status  Result and discuss  Conclusion and future work

3. Introduction  E-learning system has become more and more popular.  Many effective assessment systems have been proposed.  Conventional test systems simply provide students a score, and do not provide adaptive learning guidance for students.

4.  Intelligent Tutoring Systems (ITS) Adaptive learning. Difficult and time consuming to assess student ’ s knowledge level or learning status for the teachers manually.

5. Architecture of the system  Authoring module teachers can use to write their assignments or questions  Training/test module designed mainly for student ’ s client.  Monitor module used by instructors to keep track of student ’ s status.  Grading module assesses student ’ s knowledge map.

6. Authoring module  Manage question storage and make the schedule of a test.  Question storage is composed of the questions, answers, evaluation criteria, degree of complexity, and difficulty.  Relation strengths between concepts and the questions.

7. Training/test module  Web-Based online training/test module is designed mainly for students.  Features Client side control Time control Security control Auto-installation

8. Monitor module  The real-time monitor module keeps track of student ’ s registration, submission and performance.  Feedback including score, missing concepts, and next step help.

9. Grading module  Use the fuzzy match algorithm.  Automatically grade student ’ s answers, discriminate understanding or misunderstanding concepts of students.  Finally, we use rule inference method to create learning guidance for the learner.

10. Knowledge map

11.  If W1=0.3,W2=0.1,W3=0.6, the conditional probabilities of sub-section1

12. Diagnosing learning status  Nodes represent student ’ s answer (right or wrong).  BNs can absorb the evidence when students answer a question.

13. Learning guidance Stage1 :calculates degrees of P. Stage2 :select the max subjection degree and sends it to students.

14.  Giving advice of next step EX: C1 and C2 are the prerequisite concepts of C3. If G is less than predefined threshold value.

15. Result and discuss

16. Conclusion and future work  In this paper, presented an integrated approach to diagnose student ’ s learning status and provide learning guidance.  In the future, technology of student modeling is worth studying deeply to improve the accuracy of knowledge map representation.