Ranking and Recommendation Based on Usage Data

1. Baha' Harasheh, JadNajjar1, 2, RashidJayousi • 1Al-Quds University, • Jerusalem, Palestine • 2Eummena, Belgium Ranking and Recommendation Based on Usage Data Workshop on Learning Analytics for Collections, Repositories & Federations LACRO’13 Leuven, Belgium 9 April, 2013

2. Work and Scope • Scope: • Usage data, user traces. • Data mining techniques and algorithms. • Automatic creation of user profile. • Ranking: sort learning objects by relevance. • Recommendation: suggest learning objects. • Consolidated Framework: • Abstracts different methodologies. • Allow integration of new methods or data. • Combine results of all methods into one single result.

3. Framework

4. User Profile • Contexts: • Courses and institutions • Learning Interests • Interactions with courses • Search Objectives • Interactions with search engine (Ariadne Widget) • Relations: • Similar courses • Similar learning interests • Similar search objectives

5. Gathering and Analysis System (GAS)

6. Moodle2Cam • Log data from Moodle • User’s enrolments into courses • Attachments and files • User’s interactions with search engine

7. AutoProfileBuilder

9. Recommendations

10. Validation and Evaluation • Data Mining Algorithms: 130,000 transactions

11. Validation and Evaluation – Cont. • Precision and Recall: • 15 users • 13 courses • Analysis for system logs • True Positive, False Positive, False Negative • Results: • Precision = TP / ( TP + FP ) = 32 % • Recall = TP / ( TP + FN ) = 51 %

12. Validation and Evaluation – Cont. • SUS (System Usability Scale): • 15 users • 10 questions • Results: • Average score = 64.5 % • 7 comments collected for improvements

13. Validation and Evaluation – Cont. • Importance of Ranking and Recommendation Factors: • Courses enrolled in. • Interactions with course materials. • Previous search keywords. • Results: • All factors have similar importance (from 3.5 to 4 (scale from 0 to 5)

14. Thank You