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Interpretable early warning recommendations in interactive learning environments: a deep-neural network approach based on learning behavior knowledge graph

Education

Interpretable early warning recommendations in interactive learning environments: a deep-neural network approach based on learning behavior knowledge graph

X. Xia and W. Qi

This groundbreaking research by Xiaona Xia and Wanxue Qi tackles inefficient learning behaviors in interactive environments, introducing interpretable early warning recommendations. By utilizing a deep-neural network model centered on a learning behavior knowledge graph, the study unveils effective strategies for timely interventions and enhanced learning outcomes.

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Playback language: English
Abstract
This study addresses the issue of inefficient or ineffective learning behavior in interactive learning environments by proposing interpretable early warning recommendations. A deep-neural network model based on a learning behavior knowledge graph is designed to analyze massive learning behavior instances and their relationships. Experiments demonstrate the model's feasibility and reliability, highlighting the importance of multi-factor analysis at different temporal sequences to identify key intervals for effective learning behavior recommendations or interventions.
Publisher
Humanities and Social Sciences Communications
Published On
May 25, 2023
Authors
Xiaona Xia, Wanxue Qi
Tags
learning behavior
deep-neural network
knowledge graph
early warning recommendations
multi-factor analysis
temporal sequences
effective learning

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