Self-paced Learning with Identification Refinement for SPOC Student Grading

摘要：

　　Outliers in student activities record will cause misjudgment on student grading in online courses,especially in a SPOC course due to increased student flexibility.These outliers increased model complexity dramatically while grading students.Inspired by the process of human knowledge construction,Self-paced Learning Model which starts with an easy sample and gradually incorporates complex samples into the objective function optimization give potential to deal with high complexity.However,traditional Self-paced Learning Models use dichotomous weights to classify samples into easy ones or complex ones only.The accuracy of the model could be decreased due to the change of sample boundary caused by outliers.To this end,an identification refinement learning model is proposed and then is applied to online SPOC student grading experiments.The matrix factorization and classification experiments verify that the algorithm can improve the robustness and accuracy of the model.

关键词： Online Learning Matrix Factorization Self-Paced Learning

作者: Kexin Li Bin Xu Kening Gao Dan Yang Mo Chen

作者单位: Northeastern University Shenyang, China

会议类型: 国际会议

会议名称: 2018中国图灵大会(ACM Turing Celebration conference-China 2018)

会议地点: 上海

会议语种:英文

页码: 79-84

在线出版日期: 2018-05-19（万方平台首次上网日期，不代表论文的发表时间）