Transcript 下載/瀏覽Download

Auto Diagnosing: An
Intelligent Assessment System
Based on Bayesian Networks
IEEE 2007 Frontiers In Education ConferenceGlobal 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 knowledge level or learning status for
the teachers manually.
Architecture of the system

Authoring module


Training/test module


designed mainly for student’s client.
Monitor module


teachers can use to write their assignments or
questions
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
wrong).
 BNs can absorb the evidence when
students answer a question.

Learning guidance
Stage1 :calculates degrees of P.
Stage2 :select the max subjection degree and
sends 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
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.