Evaluation for Instructional Interaction Using Bipartite Network Representation Learning

With the combination and development of “Internet plus Education”, online education has become an important teaching mode at present. Research shows that the interaction in online education provides effective help for learners. And the evaluation of interaction is the key to achieving high-quality online learning. The interaction between learners and learning resources in online education builds a bipartite interactive network, and network representation learning technology is a powerful tool for network modeling and prediction. Based on the above analysis, an evaluation algorithm based on bipartite interactive network representation learning (EABINRL) is proposed. This algorithm combines the topological structure information of the bipartite interactive network and the interactive information between nodes, and the aim of this algorithm is to learn the low-dimensional vector representations of two types of nodes by modeling the explicit interaction behavior and the implicit interaction behavior. For different interaction types, different weights are used. Then the model is further optimized and the interactive evaluation results are obtained through Frobenius norm calculation. The results of the learner state prediction experiments conducted on the real public datasets prove the effectiveness of this algorithm.