Dynamic Student Classiffication on Memory Networks for Knowledge Tracing
Knowledge Tracing(KT)is the assessment of students knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process.KT leverages machine learning and data mining techniques to provide better assessment,supportive learning feedback and adaptive instructions.In this paper,we propose a novel model called Dynamic Student Classification on Memory Networks(DSCMN)for knowledge tracing that enhances existing KT approaches by capturing temporal learning ability at each time interval in students long-term learning process.Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art KT modelling techniques.
Massive open online courses Knowledge tracing Key-value memory networks Student clustering LSTMs
Sein Minn Michel C.Desmarais Feida Zhu Jing Xiao Jianzong Wang
Polytechnique Montreal,Montreal,Canada Singapore Management University,Singapore,Singapore Ping An Technology(Shenzhen)Co.,Ltd.,Shenzhen,China
国际会议
澳门
英文
163-174
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）