Novel Approaches for Searching and Recommending Learning Resources

Abstract

Abstract This study proposes models for searching and recommending learning resources to meet the needs of learners, helping to achieve better student performance results. The study suggests a general architecture for searching and recommending learning resources. It specifically proposes (1) the model of learning resource classification based on deep learning techniques such as MLP; (2) the approach for searching learning resources based on document similarity; (3) the model to predict learning performance using deep learning techniques including learning performance prediction model on all student data using CNN, another model on ability group using MLP, and the other model on per student using LSTM; (4) the learning resource recommendation model using deep matrix factorization. Experimental results show that the proposed models are feasible for the classification, search, ranking prediction, and recommendation of learning resources in higher education institutions.