1. Research Proposal Defense
A Personalized Web Content Recommendation System for e-Learners in E-Learning Environment.
Master of Philosophy
University of Colombo School of Computing
Name of the student: H.M.D.S Herath
Supervisor: Dr Lakshman Jayarathne
2017 December 11

2. Contents Introduction Research Purpose Literature Review
Research Questions
Expected Contribution
Scope
Proposed Methodology
Deliverables
References

3. Introduction E-Learning
The delivery of a learning, training or education program by electronic means. E-learning involves the use of a computer or electronic device (e.g. a mobile phone) in some way to provide training, educational or learning material. (Derek Stockley 2003).
Recommender systems
Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information fragment out of large amount of dynamically generated information according to user’s preferences, interest, or observed behavior about item.
Personalization
Personalization is the process of tailoring pages to individual users' characteristics or preferences.

4. Introduction
With the rapid growth of the Internet, e-Learning systems are efficiently used for education and training.
However, most of e-Leaning systems have not been personalized, several works have addressed the need for personalization in e-Learning domain.
Personalization plays a significant role in e-Learning system. This needs learner profile due to different preferences, learning activities among learners. Due to huge amount of learning resources on the web, it is hard to find learning resources related to learner request.
Therefore, recommendation systems offer suitable learning resources based on learner profile. recommendation systems help learners to reduce the overload of information that they suffer nowadays.

5. Research Purpose
Majority of e-Learning systems provide static information to the e-Learners. That means all e-Learners are getting same web contents not regarding to their own level of knowledge, preferences and activities.
Those systems are not closely monitoring e-Learners and do not identify actual requirements and activities of e-Learners.
Current Issues in e-Learning Environment
Most of e-Leaning systems provide teaching rather than learning
The amount of time spent searching for right content
Inadequate search technique for searching the learning resources
The absence of personalization in current e-Learning system
Offering appropriate learning resources to the right learner in a correct way
Predicting learner performance and tracking the progress are another challenges in current e-Learning system

6. Research Purpose
Giving solutions of above issues are a challenge. In this research, a web content recommendation system is proposed to solve problems related to e-Learners.
A proposed system with a personalized and adaptive features will introduce to e-Learners to promote e-Learning education. It is more dynamic, intelligent and it uses to identify e-Leaner's level of knowledge, e-Learners' navigation patterns effectively, preferences.
And it checks association between accessed web contents and e-Learners, relation between visited web contents and results, detecting unattended and incomplete practical tests and assignments, analyzing feedbacks and reviews, monitoring progress, predicting e-Learners’ results and satisfaction.

7. Literature Review
The personalized recommendation system in e-Learning has been widely discussed in the past decades and remain the focus of attention of many researchers today.
Number of research approaches has been analyzed to get idea about personalization, recommendation techniques, educational data mining, learner’s behaviors, predication techniques etc.

8. Literature Review
Che-Yu Yang, Hun-Hui Hsu and Jason C. Hung [1] - Web Content Suggestion System for Distance Learning.
It combines a content-based and a collaborative filtering content suggesting System filters web pages according to content analysis and creates usage profiles for student groups with similar interests.
The main idea of this system was through the utilization of information technology and the content of the web page resource, to not only provide what may be insufficient in the classroom teaching, but also through the automated web content suggestion procedure, to extend students’ interests and enrich their knowledge.

9. Literature Review
Michal Munka, Martin Drlíka [2] "Impact of Different Pre-Processing Tasks on Effective Identification of Users’ Behavioral Patterns in Web-based Educational System" focused on the processes involved in the data preparation stage of web usage mining.
Their objective to specify the inevitable steps that were required for obtaining valid data from the stored logs of the web-based educational system.
They compared datasets of different quality obtained from logs of the web-based educational system and pre-processed in different ways:
Data with identified users’ sessions
Data with the reconstructed path among course activities.
They tried to assess the impact of these advanced techniques of data pre-processing on the quantity and quality of the extracted rules that represent the learners’ behavioral patterns in a web-based educational system.

10. Literature Review
Essaid E, Bachari , El H, Abelwahed and Mohammed E, Adnani [3] “E-Learning personalization based on Dynamic learners' preference” proposed a personalized e-learning system - LearnFit which can which takes the dynamic learner’s personality into account.
Proposed framework comprised following three elements:
1. Domain Model: Consist of concepts and the relations that exist between them. Typically the domain model gives a domain expert’s view.
2. Learner Model: Consists of relevant information about the user that is pertinent to the personalization of the learning style.
3. Pedagogical Model: includes two parts :
Adaptive Engine Model: Consists of set of rules or triggers for describing the runtime behavior of the system as well as how the domain model relates to the user model to specify adaptation.
Revised Strategy Model: Consists to determine whether a given resource is
appropriate for a specific learning style or not.

11. Literature Review
Nguyen Thai-Nghe, Lucas Drumond, Artus Krohn-Grimberghe, and Lars Schmidt-Thieme [4] “Recommender System for Predicting Student Performance” proposed a novel approach which uses recommender system techniques for educational data mining, especially in predicting student performance.
They also proposed how to map the educational data to user/item in recommender systems. To validate this approach, they compared recommender system techniques with traditional regression methods such as logistic regression by using educational data.
Experimental results showed that the proposed approach can improve the prediction results.

12. Literature Review
Most of the previous studies focused on content-based and collaborative filtering for creating learners’ profile based on learners’ references.
Web mining techniques were used to identify learners’ behaviors and make predications.
In this research, content-based, collaborative filtering and web mining techniques are adopted and suggest an effective mechanism to identify the learner’s level of knowledge using implicit feedbacks based on different tests.
Then proposed system can make suitable recommendations based on above mentioned factors.

13. Research Questions
Question 1: “How does web content map with e-Learner in recommendation processes? [1][8][9]”.
The proposed approach can solve this problem by implementing the content-based and collaborative filtering approach. In content-based filtering the e-Learners are recommended relevant web contents that are similar to the once they preferred or accessed or liked in past. Collaborative filtering, the e-Learners are recommended relevant web contents that are similar to the other e-Learners’ preferred or accessed or liked in past.

14. Research Questions
Question 2: “Is there an association between e-Learners’ navigational pattern and performance [2][3][10]”.
To address this, pre-processed data with identified e-Learners’ sessions have been compared with their results to get association.

15. Research Questions
Question 3: “What are the factors that affecting to weak performance by e-Learners in different web contents [3]”.
The proposed system can be identified e-Learners’ navigation patterns by using web mining steps. Those patterns, e-Learners’ activities and preferences are compared with practical test and assignment results to derive the factors which affected to performance.

16. Research Questions
Question 4: “How does e-Learner motivate to do more interactions with the system? [3][5]”.
E-Learner profiling for all e-Learners can address this. Since e-Learner profile is created based on navigational patterns, preferences etc. for each and every e-Learners. This system can recommend learning contents based on the e-Learners’ profile.

17. Research Questions
Question 5: “How the system does identify the actual e-Learner’s level of knowledge? [5][6][10]”.
The proposed system is planning to use questionnaires to identify the level of knowledge in different stages such as initial, practical and final.
However, both content-based and collaborative filtering suffer cold-start problem. This problem happens in cases where there is a lack of information about e-Learners and their preferences in past which makes it impossible to provide relevant recommendations [6][7].
Related fifth question, the proposed system is going to introduce different level of quizzes, based on initial level questionnaire can be solved cold-start problem.

18. Research Questions
Question 6: “How the system does predict the e-Learner’s results in different tests and track the progress? [4][7]”.
In this research, regression method is used to predict results and subsystem is introduced to check progress of each e-Learners based on above mentioned method.

19. Expected Research Contributions
Recommending suitable web contents based on e-Learners skill levels and learning activities.
Providing point indicators to encourage e-Learners to actively participate with e-Learning environment.
Encouraging e-Learners to stick with self-learning style.
Motivating e-Learners to do more questions based on weakness area and improve their knowledge.
Providing a facility for e-Learners to check progress individually, compare with other e-Learners and predict future result.
Creating a virtual environment to collaborate with other e-Learners using topic discussions, event participations, Questions and Answers.

20. Scope of the study
These are the area to cover in a personalized web recommendation system
Courses, concepts and learning objects
E-Learners details,login and log information
Questions (Skill/Practical/Assignment)
Result records
Enrollments and Attempts
System Users (Instructional Designers/Lecturers/Admin )
Bookmarks (Learning Objects/Web Contents) and wish list (Courses)
Web Contents (Articles/Videos/Files)
E-Learners’ preference details
E-Learners’ access contents
E-learners’ reviews and feedbacks
Topic Discussions by E-Learners
Generating recommendations for e-Learners
Questions and Answers by E-Learners and System user
Sentiment analysis for published web contents
Events participations and locations adding
Notifications
Generating reports for decision making purposes

21. Proposed Methodology
A proposed Framework for A Personalized Web Content Recommendation System

22. Proposed Methodology
Generally, the proposed system consists of three main components
Domain Model,
Learner Model
Recommender Model.

23. Proposed Methodology
Domain Model
A domain model contains the knowledge about the curriculum structure. This model is split into three layers, the first represents the course and each course is divided on several concepts, and each concept is presented by a set of learning objects.

24. 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

25. Test Name
No of Test per Course
No of Questions per Test
No of Attempts
Initial Skill
No of concepts per Course (nCn)
nCn * 5 1
Practical
No of Learning Objects per Course (nLo)
nLo * 5
any
Final Skill
nCn * 10
Maximum 2
Assignment
nCn * nLo * 5

27. Proposed Methodology Leaner Model
The learner model represents the various characteristics of the learner such as personal information, preferences, navigational patterns, accessed contents, level of knowledge, etc. which can be used to generate an individualized learning experience .
In our research, learner who enroll with a particular course is going to take questions (Initial skill level test) to determine the initial level of knowledge and build the learner profile. Apart from that, learner preferences are used to present the learner profile as well as.

28. Proposed Methodology

29. Proposed Methodology Recommendation Model
The proposed recommendation model has two modules, respectively.
1) recommender module and
2) prediction module.

30. Proposed Methodology
Recommender module helps to generate suitable recommendation to learners based on learning activities.
This module uses content-based filtering and collaborative filtering to do that. First we apply the content-based filtering approach, the term vector is submitted in order to compute recommendation list. Results are ranked according to the cosine similarity of their content (vector of TF-IDF weighted terms) with submitted term vector.
Second, we apply the collaborative approach in order to classify the active learner in one of the learner’s group

31. Proposed Methodology
Finally, prediction module in recommendation model predicts future results for the different tests. Learners can check progress individually and compare with other learners. To implement prediction module, the system is used liner regression algorithm.

32. Recommendation System Content-Based Filtering Collaborative Filtering
Proposed Methodology
Recommendation System
Content-Based Filtering
Collaborative Filtering
Term Frequency /Inverse Document Frequency(TF/IDF)
Model-based
Memory- based
Clustering
Regression
Decision tree
Association rule
User-based
Item-based
Similarity Measures-Cosine-based

33. Work Plan and Deliverables

34. [1] C. Y. Yang, H. H. Hsu, and J. C. Hung, "A Web Content Suggestion System for Distance Learning," 2006.
[2] M. Munk and M. Drlík, "Impact of Different Pre-Processing Tasks on Effective Identification of Users’ Behavioral Patterns in Web-based Educational System", Procedia Computer Science, vol. 4, pp , 2011.
[3] E. Bachari, E. Abelwahed and M. El Adnani, "E-Learning personalization based on Dynamic learners' preference", International Journal of Computer Science and Information Technology, vol. 3, no. 3, pp , 2011.
[4] N. Thai-Nghe, L. Drumond, A. Krohn-Grimberghe and L. Schmidt-Thieme, "Recommender system for predicting student performance", Procedia Computer Science, vol. 1, no. 2, pp , 2010.
[5] M. zare, R. Sarikhani, M. Salari, V. Mansouri, "The impact of e-learning on students' academic achievement and creativity", Journal of Technical Education and Training, vol. 8, no. 1, pp , 2016.
[6] M. zare, R. Sarikhani, M. Salari, V. Mansouri, " The Influence of Learning Styles on Learners in E-Learning Environments: An Empirical Study", SCRIBD, 2006.
[7] M. Al-Barrak and M. Al-Razgan, "Predicting Students Final GPA Using Decision Trees: A Case Study", International Journal of Information and Education Technology, vol. 6, no. 7, pp , 2016.
REFERENCES

35. [8] S. Aher, l. Lobo , "COURSE RECOMMENDER SYSTEM IN E-LEARNING",International Journal of Computer Science and Communication, vol. 3, no. 1, pp , 2012.
[9] O. Bourkoukou, E. Bachari and M. Adnani, "A Personalized E-Learning Based on Recommender System", International Journal of Learning and Teaching, 2016.
[10] "Personalized Recommendation System Modeling in Semantic Web", INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences, vol. 5, no. 2, pp , 2013.
[11] A. TALAKOKKULA, "A Survey on Web Usage Mining, Applications and Tools," Computer Engineering and Intelligent Systems, Vol.6, No.2, 2015.
REFERENCES

36. Questions and Answers?????
Thank You