Abstract:

Focusing on the prediction of student grade in the undergraduate teaching of higher education， a prediction algorithm based on course Knowledge Graph （KG） was proposed. Firstly， a course KG representing course information was constructed. Then， the neighbor-based methods and the KG representation learning-based methods were used to calculate the similarity of the courses on the knowledge level based on the KG， and those knowledge similarities among courses were integrated into the traditional grade prediction framework Collaborative Filtering （CF）. Finally， the performance of the algorithm with fusing KG and the common prediction algorithm in different data sparsities were compared in experiments. Experimental results show that in the data sparse scenario， compared with the traditional CF algorithm， the neighbor-based algorithm has the Root Mean Square Error （RMSE） reduced by about 11% and the Mean Absolute Error （MAE） reduced by about 9%； and compared with the traditional CF algorithm， KG representation learning-based algorithm has the RMSE reduced by about 17.55% and the MAE reduced by about 11.40%. Experimental results indicate that the CF algorithm using KG can significantly reduce the prediction error， which proves that the KG can be used as information supplement in the lack of historical data， thus helping CF to obtain better prediction results.

Key words: Collaborative Filtering (CF), Knowledge Graph (KG), grade prediction, Educational Data Mining (EDM), intelligent campus

摘要：

针对高等教育本科教学场景中的学生成绩预测问题，提出了一种基于课程知识图谱（KG）的预测算法。首先，构造一个表示课程信息的课程知识图谱。然后，分别使用基于邻节点的方法和基于知识图谱表示学习的方法基于知识图谱计算课程在知识层面的相似度，并将课程的知识相似度集成到传统的成绩预测框架协同过滤（CF）中。最后，通过实验对比了融合知识图谱的算法和常见成绩预测算法在不同数据稀疏度场景下的性能。实验结果显示，在数据稀疏场景下，基于邻节点的算法和传统协同过滤算法相比，均方根误差（RMSE）下降约11%，平均绝对误差（MAE）下降约9%；基于图谱表示学习的算法与协同过滤算法相比RMSE下降17.55%，MAE下降11.40%。实验结果表明，运用知识图谱的协同过滤算法可使预测误差显著下降，验证了知识图谱可以作为历史数据缺乏场景下的信息补足，从而帮助协同过滤获得更好的预测效果。

关键词: 协同过滤, 知识图谱, 成绩预测, 教育数据挖掘, 智慧校园