Learning behaviour prediction and multi-task recommendation based on a knowledge graph in MOOCs

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ABSTRACT One challenging issue in improving the teaching and learning methods in MOOCs is to construct potential knowledge graphs from massive learning resources. Therefore, this study proposes knowledge graphs driving online learning behaviour prediction and multi-learning task recommendation in MOOCs. Based on the knowledge graphs supported by multi-entities and features, the authors designed a novel deep learning model to fully calculate and optimise iterative learning processes, explore key semantics and potential values of learning behaviours, integrate multi-task items and flexibly analyse learners’ multi-entities and related features. Assisted by knowledge concept semantics and the formation of knowledge graphs, multi-entities of learning behaviours are fused and associated, which might be the leading trend of knowledge graphs. Sufficient experiments have proved that the whole work might ensure learning behaviour prediction and multi-task selection that can enable the knowledge graph to improve learning behaviours in MOOCs.