auto diagnosing: an intelligent assessment system based on bayesian networks ieee 2007 frontiers in...
TRANSCRIPT
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.