Mining e-Learning domain concept map from academic articles

1. Mining e-Learning domain concept map from academic articles Presenter : Yu-hui Huang Authors :Nian-Shing Chen , Kinshuk , Chun-Wang Wei , Hong-Jhe Chen 國立雲林科技大學 National Yunlin University of Science and Technology CE 2008

2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

3. Motivation • When you start to research a new domain knowledge , you maybe not understand that how to beginning . • To provide a learning directions for the beginner by navigation tool such as concept map. • In the past the concept map is constructed by a group domain experts.

4. Objective • The concept maps can provide a useful reference for researchers. • Adaptive learning： To provide really relation issue for researches. • To reflect the relation strength between any two keywords appeared in the articles.

5. Methodology • To construct the concept map by following four essential assumptions. Assumption 1.Each keyword listed in a research article represents one essential concept. Assumption 2. If two keywords appear in one research article, it implies that certain relation exists between these two keywords. Assumption 3. The higher the frequency of occurrences of two keywords appeared in one sentence, the higher the relation would be between them. Assumption 4. The shorter the ‘‘distance’’ between two keywords in one sentence, the higher the relation would be between them.

6. Methodology • System design and methodology • Data source： • Journalfrom 1999 to 2004 • Conference from 2001 to 2004 • Concept item extraction • Step1：Keyword clearing (ex：eLearning, e-Learning and E Learning) • Step2：Acronym mapping (ex：IDSL) • Step3：Suffix strippinig (-ed, -ing, -ion, and -ions)

7. Methodology • Research keyword indexing by PCA • Evaluate importance’s variables as follows ： • related counts： number of other keywords that appeared in the same sentence with the research topic. • appeared times ： number of times a keyword appeared in an article. • Sustained periods：keyword appeared from the first time to the last time.

8. Methodology • Calculation of relation strength. (According to the third and fourth assumptions)

9. Experiments

10. Experiments

11. Conclusion • In this study, initially, reducing the original keywords to nearly 50%. • Instructors can also use concept maps to provide adaptive learning materials and design adaptive learning paths to guide learners. • And guide student what other topics they can learn which have high ‘‘relation strength’’ with the current topic. • Future directions： • (1) expanding the range of data to construct a more robust concept map for the e-Learning domain; • (2) in addition to the three parameters that were used in principal component analysis, other parameters, such as taxonomy of keywords and timing can be added for representing the importance of the research keywords. • (3) improving the formula of ‘‘relation strength’’ by considering intrinsic meaning. 11

12. Comments • Advantage • This concept map can provide adaptive learning. • Drawback • …. • Application • E-learning