A Hybrid E-learning Recommendation Approach Based on Learners’ Influence Propagation

 Abstract:

            In e-learning recommender systems, interpersonal information between learners is very scarce, which makes it difficult to apply collaborative filtering (CF) techniques. a hybrid filtering (HF) recommendation approach (SI - IFL)combining learner influence model (LIM), self-organization based (SOB) recommendation strategy and sequential pattern mining(SPM) together for recommending learning objects (LOs) to learners a hybrid filtering (HF) recommendation approach (SI - IFL)combining learner influence model (LIM), self-organization based (SOB) recommendation strategy and sequential pattern mining(SPM) together for recommending learning objects (LOs) to learners.LIM is applied to acquire the interpersonal information by computing the influence that a learner exerts on others. LIM consists of learner similarity, knowledge credibility, and learner aggregation. LIM is independent of ratings.SOB recommendation strategy is applied to recommend the optimal learner cliques for active learners by simulating the influence propagation among learners. Influence propagation means that a learner can move toward active learners, and such behaviours can stimulate the moving behaviours of his neighbours. This SOB recommendation approach achieves a stable structure based on distributed and bottom-up behaviours of individuals. The final learning objects (LOs) and navigational paths based on the recommended learner cliques. The experimental results demonstrate that SI - IFL can provide personalized and diversified recommendations, and it shows promising efficiency and adaptability in e-learning scenarios.

SOFTWARE REQUIREMENTS:

Operating system  :  Windows 7/10

Coding Language  :  JAVA/J2EE

IDE                        : Net beans 8.0.1

Database                : MYSQL 5.52