Abstract: 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.

Key words: representation learning, online education, interactive evaluation, big data in education

摘要： 随着“互联网+教育”的结合与发展，在线教学成为当前的重要教学模式。研究表明，在线教学中的交互为学习者提供了有效的帮助和支持，而得到对交互的评价反馈是实现高质量在线学习的关键。在线教学中的学习者与学习资源的交互构成了一个二分交互网络，而网络表示学习技术则是对网络建模和预测的强有力工具。基于上述分析，提出基于二分交互网络表示学习的评价方法（EABINRL），该方法旨在结合二分交互网络的拓扑结构信息与节点之间的交互信息，通过对显式的交互行为和隐式的交互行为两部分进行建模以学习两种类型节点的低维向量表示，其中针对不同的交互类型采用了不同的权重。而后进一步优化模型流程，最终得到基于F-范数计算的交互评价结果。在真实公开的数据集上进行的学习者状态预测实验的结果证明了该方法的有效性。

关键词: 表示学习, 在线教学, 交互评价, 教育大数据