learning portfolio analysis and mining for scorm compliant environment

Intelligent Database Systems Lab

N.Y.U.S.T.I. M.

Learning Portfolio Analysis and Mining for SCORM Compliant Environment

Pattern Recognition (PR, 2010)

Presenter : Su, Wun-HueiAuthors : Jun-Ming Su, Shian-Shyong Tseng, Wei Wang and Jui-Feng Weng

Jin Tan David Yang Wen-Nung Tsai


N.Y.U.S.T.I. M.

2

Outline

Motivation Objective Methodology Implement Experiments Conclusion Comments


N.Y.U.S.T.I. M.Motivation

With vigorous development of the Internet, e-learning system has become more and more popular. Sharable Content Object Reference Model (SCORM, 2004)

how to provide customized course how to create, represent and maintain the activity tree Learning portfolio can help teacher understand the

reason why a learner got high or low grade

3


N.Y.U.S.T.I. M.

4

Objectives

we apply data mining approaches to extract learning features from learning portfolio and then adaptively construct personalized activity trees


N.Y.U.S.T.I. M.Methodology – Overview

5

The Framework of Learning Portfolio Mining (LPM)


N.Y.U.S.T.I. M.

Methodology – User Model Definition Phase

6

Learner L= (ID, LC, LS) LC = <c1c2…cn> LS = <s1s2…sn> L=(35, <F, M, S Y, H, FD, D, T, H>, < A, AA, AAA, AAB, AB>)


N.Y.U.S.T.I. M.

Methodology – Learning Pattern Extraction Phase

7

Learning Pattern Extraction Phase


N.Y.U.S.T.I. M.


8

Sequential Pattern Mining Process We use GSP algorithm to extract the frequent learning patterns from

learning portfolio


N.Y.U.S.T.I. M.


9

Feature Transforming Process based upon maximal learning patterns in Table 3, the original learning

sequences of every learner can be mapped into a bit vector


N.Y.U.S.T.I. M.


10

Learner Clustering Process we can apply clustering algorithm to group learners

into several clusters according to learning features of learners K-means algorithm(it difficult determine the number of clusters ) ISODATA clustering approach to group learners into different

clusters(can dynamically change the number of clusters by lumping and splitting procedures and iteratively change the number of clusters for better result)


N.Y.U.S.T.I. M.

Methodology – Decision Tree Construction Phase

11

how to assign a new learner to a suitable cluster according to her/his learning characteristics and capabilities is an issue to be solved we can apply decision tree induction algorithm, ID3 (Quinlan,

1986), to create a decision tree.


N.Y.U.S.T.I. M.


12

Activity Tree Generation Phase


N.Y.U.S.T.I. M.Implementation

13


N.Y.U.S.T.I. M.Implementation

14


N.Y.U.S.T.I. M.Experimental

15


N.Y.U.S.T.I. M.

16

Conclusions

How to provide customized course according to individual learning characteristics , and how to create the activity tree in SCORM 2004

we propose a four phase Learning Portfolio Mining (LPM) Approach predict which group a new learner belongs to also propose an algorithm to create personalized activity tree which can

be used in SCORM compliant learning environment.

The analysis of experimental results by performing the t-test also shows that this LPM approach is workable and beneficial for learners


N.Y.U.S.T.I. M.

17

Comments

Advantage A good application

Drawback

Application Analysis portfolio record of e-learning system and provide learners with

more personalized learning guidance

learning portfolio analysis and mining for scorm compliant environment

Documents