1. E-learning Activities Recommender System (ELARS)
Department of Informatics, University of Rijeka
Radmile Matejčić 2, Rijeka, Hrvatska
Tel.: Fax:
E-learning Activities Recommender System (ELARS)
Nataša Hoić-Božić, Martina Holenko Dlab
Division of multimedia systems and e-learning .

2. Outline Introduction ELARS recommender system
System structure
Experimental results and Conclusions
Future plans - "E-learning Recommender System" project
DAAD WS 2014
26/8/2014

3. Introduction Collaborative learning activities (e-tivities)
Web 2.0 tools  interactivity
Recommender systems  personalization
PhD thesis: Recommender system for activities in computer-supported collaborative learning (defended in July 2014)
Supervisors: Professor Vedran Mornar and Associate Professor Nataša Hoić-Božić
DAAD WS 2014
26/8/2014

4. ELARS - E-Learning Activities Recommender System
Personalization of collaborative e-learning activities performed using Web 2.0 tools
recommendations for students and groups before and during e-tivities
Activity level estimation  quantity and continuity of student’s (group’s) contributions
enable recommendations generation
support teachers in evaluation of students’ work
DAAD WS 2014
26/8/2014

5. ELARS system structure
DAAD WS 2014
26/8/2014

6. Activity model Course activities: Items for recommendations:
classified (6 different types)
grouped in learning modules
Items for recommendations:
Optional e-tivities
Web 2.0 tools
Collaborators
Advice
Mind mapping
Your activity level is not satisfying so it is highly recommended that you participate to a greater extent.
DAAD WS 2014
26/8/2014

7. Activities workflow example
DAAD WS 2014
26/8/2014

8. Student and group models
Student’s characteristics
Learning styles (VARK model)
Web 2.0 tools preferences
Knowledge level
Activity level
Group’s characteristics
DAAD WS 2014
26/8/2014

9. Subsystem for generating recommendations
Ranking items according to usefulness
support students in decision
Filter appropriate advice from pre-defined set
Techniques adapted to the educational domain
include pedagogical rules
teachers can modify the recommendation criteria
DAAD WS 2014
26/8/2014

10. Recommending offered optional e-tivities
Usefulness: similarity student ↔ e-tivity (teachers criteria)
Technique: content-based
0.80
0.67
0.50
student
group
DAAD WS 2014
26/8/2014

11. Recommending possible collaborators
Usefulness: similarity (teachers criteria)
Technique: content-based
0.90
0.87
0.86
0.82
Homogeneous
0.33
0.45
0.67
0.90
Heterogeneous
DAAD WS 2014
26/8/2014

12. Recommending Web 2.0 tools offered for certain e-tivity
Usefulness: student’s preference of the target tool (prediction of missing values)
Technique: hybrid (collaborative filtering content-based)
student
group
0.90
0.32
-0.5
DAAD WS 2014
26/8/2014

13. Providing advice Technique: knowledge-based Useful/not useful
DAAD WS 2014
26/8/2014

14. ELARS web application http://161.53.18.114/elars DAAD WS 2014
26/8/2014

15. ELARS demo
DAAD WS 2014
26/8/2014

16. Learning modules and activities
DAAD WS 2014
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17. Decision activities
DAAD WS 2014
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18. Activity levels and advice
DAAD WS 2014
26/8/2014

19. Experimental results and Conclusion
Evaluation focused on pedagogical aspects
two e-courses (University of Rijeka, Croatia)
control and experimental group:
better results (points) for e-tivities
survey:
students are satisfied with received recommendations and find the system useful
Good results on experimental courses and students’ satisfaction provide motivation for further work and improvements.
DAAD WS 2014
26/8/2014

20. Future plans - "E-learning Recommender System" project
"E-learning Recommender System" project supported by University of Rijeka
Future plans
recommendations algorithms improvements
improvements of ELARS authoring component for teachers
creating and evaluating didactical models for the use of Web 2.0 based e-tivities in different types of blended and online higher education e-courses
DAAD WS 2014
26/8/2014

21. Thank you!