Applying Clustering to the Problem of Predicting Retention within an ITS: Comparing Regularity Clustering with Traditional Methods.
Fei Song, Shubhendu Trivedi, Yutao Wang, Gábor N. Sárközy, Neil Thomas Heffernan · 2013
In student modeling, the concept of “mastery learning ” i.e. that a student continues to learn a skill till mastery is attained is important. Usually, mastery is defined in terms of most re-cent student performance. This is also the case with models such as Knowledge Tracing which estimate knowledge solely based on patterns of questions a student gets correct and the task usually is to predict immediate next action of the student. In retrospect however, it is not clear if this is a good defini-tion of mastery since it is perhaps more useful to focus more on student retention over a longer period of time. This paper improves a recently introduced model by Wang and Beck that predicts long term student performance by clustering the stu-dents and generating multiple predictions by using a recently developed ensemble technique. Another contribution is that we introduce a novel clustering algorithm we call “Regularity Clustering ” and show that it is superior in the task of predict-ing student retention over more popular techniques such as k-means and Spectral Clustering.