Auto Diagnosing: An Intelligent Assessment

SystemBased 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

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

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.

Intelligent Tutoring Systems (ITS) Adaptive learning. Difficult and time consuming to assess student’s k

nowledge level or learning status for the teachers manually.

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.

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.

Training/test module Web-Based online training/test module is

designed mainly for students. Features

Client side control Time control Security control Auto-installation

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.

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.

Knowledge map

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

Diagnosing learning status Nodes represent student’s answer (right or w

rong). BNs can absorb the evidence when students a

nswer a question.

Learning guidanceStage1 :calculates degrees of P. Stage2 :select the max subjection degree and se

nds it to students.

Giving advice of next step

EX: C1 and C2 are the prerequisite concepts of C3.

If G is less than predefined threshold value.

Result and discuss

Conclusion and future work In this paper, presented an integrated approac

h 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.