The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempting the assessment activity. The developed model is real-time and tracks the participation of a student within a MOOC (via click-stream server logs) and predicts the performance of a student on the next as- sessment within the course offering. We perform a com- prehensive set of experiments on data obtained from three openEdX MOOCs via a Stanford University initiative. Our experimental results show the promise of the proposed ap- proach in comparison to baseline approaches and also helps in identification of key features that are associated with the study habits and learning behaviors of students.
翻译:过去几年来,用于分析从Mas- sive 开放在线课程(MOOCs)获得的数据的数据的微量分析方法迅速增长。本研究的目标是制定方法,根据信息预测一个学生在某一年级评估中可能达到的分数,该评估被视为课程中的先前性能或先前性能。我们开发了一个个性化线性线性微量回归模型,以便在尝试评估活动之前预测学生的分数。所开发的模式是实时的,跟踪学生在MOOC(通过点击-流式服务器日志)中的参与情况,并预测下一个学生在课程提供中作为学生的分数的成绩。我们通过斯坦福大学的一项举措对从3个开放EdX MOOCs获得的数据进行了一组共振前实验。我们的实验结果显示,与基线方法相比,拟议的p-proach(p- proach)很有希望,还有助于确定与学生学习习惯和学习行为有关的关键特征。