A RuleBased Approach for Adaptive Content Recommendation in a Personalized Learning Environment: An Experimental Analysis Rapidly growing Information and Communication Technologies (ICT) have increased the availability of multimedia learning objects (LO) in the elearning system. However, the problem is that the system is unable to allocate appropriate learning objects to the learners. Personalized learning environments make the system more adapt to the learner profile, thus improve their performance and quality of learning. The learning style (LS) of a learner is a prominent metric to understand the learner profile. This paper discusses an approach to personalize the content recommendation based on the learning style of the learner. A rulebased expert system is implemented to recommend the content to the learner, where the learner is modeled using a probabilistic learning style model, and the teaching aspects of learning objects are modeled using specific fields of IEEE Learning Object Metadata Standard. The ruleset defined in this paper is used to recommend the most relevant learning objects to the learners. Finally, the recommendations are crossvalidated with the LO ranking from a set of 48 participants and found that 75% of recommendations are compatible with the learner choice.

Learning Resource Recommendation Based on Generalized Matrix Factorization and Long ShortTerm Memory Model Online learning is becoming increasingly popular in recent years. Personalized recommendation is particularly important for the development of online learning systems. Though LSTM model has been widely applied in various recommendations, it normally canu0027t deal with the problem of sparse data. In this paper, we present a novel model for learning resource recommendation, named GLSTM. Our model integrates the Generalized Matrix Factorization (GMF) with Long ShortTerm Memory (LSTM) model. For evaluating our model, we prepare and analyze two datasets from Junyi Academy. Extensive experiments are conducted on the two datasets to verify the superiority of our model in both effectiveness and accuracy.