论文标题
基于神经网络的协作过滤问题测序
Neural Network-Based Collaborative Filtering for Question Sequencing
论文作者
论文摘要
电子学习系统（ELS）和智能辅导系统（ITS）在当今的教育计划中起着重要作用。测序问题是为目标学习者生成个性化测验的艺术。个性化的测试将丰富学习者的经验，并为更有效，更有效的学习过程做出贡献。在本文中，我们使用神经协作过滤（NCF）模型来生成问题测序，并将其与基于配对的内存问题测序算法-Edurank进行比较。 NCF模型的排名结果明显好于Edurank模型，平均精度相关得分为0.85，而0.8则具有0.8的平均值。
E-Learning systems (ELS) and Intelligent Tutoring Systems (ITS) play a significant part in today's education programs. Sequencing questions is the art of generating a personalized quiz for a target learner. A personalized test will enrich the learner's experience and will contribute to a more effective and efficient learning process. In this paper, we used the Neural Collaborative Filtering (NCF) model to generate question sequencing and compare it to a pair-wise memory-based question sequencing algorithm - EduRank. The NCF model showed significantly better ranking results than the EduRank model with an Average precision correlation score of 0.85 compared to 0.8.
