1. University of Colombo School of Computing, Colombo, Sri Lanka
A Personalized Web Content Recommendation System for e-Learners in e-Learning Environment
Daminda Herath
University of Colombo School of Computing, Colombo, Sri Lanka

2. Introduction
Nowadays e-Learning becomes one of the social aspects of the web and it plays a major role.
Education institutions, universities and business organizations use e-Learning system to disseminate resources among e-Learners.
Those parties struggle to give quality and useful required information to the e-Learners. On the other hand, e-Learners will be dissatisfied about given information and lose focus on the e-Learning.

3. Introduction
According to the current e-Learning system, some problems are identified.
Navigation patterns of e-Learners.
E-learners’ level of knowledge.
Association between subjects contents and questions.
Relation between subject contents and assignment results by e-Learners.
Tracking assignments completions and grades.
Attention about posts in forums and frequently asked questions.

4. Introduction e-Learners weakness based on contents.
Unattended assignments by e-Learners.
Analyzing the e-Learners feedback.
Monitoring the progress of e learners.
Predictions about enrollment, completions of course, accessing of contents and feedbacks.
Recommending course contents.

5. Introduction
In this research, a web content recommendation system is proposed to solve problems.
A new system with a personalized and adaptive features will be introduce to e-Learners to promote e-Learning education.
It is more dynamic and intelligent
It uses to identify e-Leaner's level of knowledge and give good recommendations.

6. Methods
To achieve this, web mining is used as a popular technique in Data mining.
Web Mining Web Content Mining
Web Usage Mining
Techniques
Clustering,Classification, Association, Sequential pattern analysis.
Recommendation Collaborative and Content Filtering

7. A proposed Framework for A Personalized Web Content Recommendation System

8. Course →Concept → Learning Object
Course Content Hierarchy

9. Levels of Question
Levels of Knowledge / Skill
Level
Proficiency
Energy Point (EP) 1
Very Easier 10 2
Easy 15 3
Medium 20 4
Hard 25 5
Very Hard 30
Energy Point Range
Proficiency
Not Applicable
1-19
Beginner
20-39
Limited
40-59
Moderated
60-79
Competence
80-100
Good
TYPES OF QUESTION
Initial Skill Level
Practical Level
Final Skill Level
Assignment Level
All Questions are based on Learning Objects

10. Result

11. Concepts

12. Learning Objects

13. Review the answers

14. Skill Level and Recommendations

15. After Skill Level Test

16. Practical Level Test

17. Practical Level Attempts and eLearners’ Emotions

18. Learners’ Feedback

19. Initial Skill Level Test Summery
Introduction to Python Learning Object

20. Test Case Step Test case Expected result Actual Outcome Status 1
Select the course
Display course
Display course web content
Pass 2
Try Initial skill level quiz
Login into the system
Enroll with the course
Display login interface
Display enroll button 3
Try practical quiz
Check whether skill test finished
Display a message
(Try practical quiz/Finish skill test) 4
Try Final skill level quiz
Check whether practical quiz finished
(Try Final Skill level quiz/Finish practical quiz)
Try assignment quiz
Check whether Final skill level quiz finished
(Try assignment quiz/Finish final skill level quiz)

21. Conclusion
In this research, a personalized web content recommendation system proposed to encourage the e-Learners to pro-actively engage in e-learning environment to improve their education.
This system was used web mining techniques such as web content and usage mining. Web content mining was used to identify relevant web contents and specifically web usage mining was used to identify e-Learners navigational patterns which could help to identify interests and weaknesses of e-Learners, frequently visited web contents, predicting performances of e-Learners.
Then recommendation system could give an effective and efficient personalized web contents with aid of content and collaboration filtering.

22. Future Works
In future work, it is going to implement for number of other courses in the e-learning environment and introduce biometric identification to identify the authorized e learners.
Apart from that web mining and semantic web technologies can be incorporated to create an enhanced system with more intelligence.
Also it is planning to implement this recommendation system with Massive Open Online Courses (MOOC). Machine learning (ML) and big data analytics techniques are used to make recommendation more accurately and enhance the performance in large heterogeneous environment.

23. Questions ??????????????
Thank You