摘要
在线个性化学习系统能够根据学生的学习历史,为学生提供个性化的学习资源,辅助学生高效学习.要提供精准的个性化诊断报告和个性化资源推荐,首先要对学生进行学业能力评估,其中一个基础性任务为得分预测.对于得分预测任务,现有的研究和方法存在如下不足:①不能充分利用大数据提升预测精度,②无法解决实际应用场景中常见的冷启动问题,③预测结果不可解释.为此提出并实现了一种基于知识图谱的自编码模型(knowledge-awareauto-encodermodel,KAEM)用于学生得分预测.首先介绍了含有教育专家先验知识的一种知识图谱,称之为锚题图谱;然后KAEM采用深度学习自编码技术,将教研对锚题图谱的先验理解作为自编码器的正则化项加入模型中,有效地解决冷启动问题.此外,此类模型的预测结果还可以解释化,为实际个性化学习推荐等应用场景提供教研依据.KAEM已经在国内某在线教育系统上运行,取得了良好的效果;在大规模数据上也实验验证了KAEM的有效性.
To reduce study burden and boost efficiency,online education systems offer personalized learning experience for students. In such systems,ability assessment is a fundamental task as reflected by a basic task,named score prediction. The main drawbacks of existing prediction methods are:① Inability unable to fully exploit the potential of big data,② cold start problem,③ lack of reasonable explanations. A novel knowledge-aware auto-encoder model (KAEM) is proposed to address these issues. Specifically,an exercise-knowledge-graph with education experts’ prior knowledge is introduced. Then students’ performance is modeled using auto-encoders with the combination of information in knowledge graph as regularization item. By encoding and integrating the experts’ prior knowledge,KAME can improve both prediction accuracy and model robustness and deal with the cold start problem well. Furthermore,reasonable explanations for recommendations can be generated using this model. KMAE has been applied to a famous online education system. Extensive experiments on large-scale real data clearly demonstrate its effectiveness.
作者
苏喻
张丹
刘青文
张英杰
陈玉莹
丁宏强
SU Yu;ZHANG Dang;LIU Qingwen;ZHANG Yingjie;CHEN Yuying;DING Chris(Department of Computer Science and Technology,Anhui University,Hefei 230039,China;iFLYTEK Co.,Ltd.,Hefei 230088,China;Anhui Province Key Lab. of Big Data Analysis and Application,University of Science and Technology of China,Hefei 230027,China)
基金
国家自然科学基金(61672483和61572030)
国家基础研究发展(973计划)(2015CB351705)
关键词
个性化学习
知识图谱
自编码
冷启动
得分预测
personalized learning
knowledge graph
auto-encoder
cold start problem
score prediction
