title: Motivation Classification and Grade Prediction for MOOCs Learners.
abstract: While MOOCs offer educational data on a new scale, many educators find great potential of the big data including detailed activity records of every learner. A learner's behavior such as if a learner will drop out from the course can be predicted. How to provide an effective, economical, and scalable method to detect cheating on tests such as surrogate exam-taker is a challenging problem. In this paper, we present a grade predicting method that uses student activity features to predict whether a learner may get a certification if he/she takes a test. The method consists of two-step classifications: motivation classification (MC) and grade classification (GC). The MC divides all learners into three groups including certification earning, video watching, and course sampling. The GC then predicts a certification earning learner may or may not obtain a certification. Our experiment shows that the proposed method can fit the classification model at a fine scale and it is possible to find a surrogate exam-taker.
authors.affiliation: Computing Center, Northeastern University, Shenyang 110819, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Xu
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
authors.forename: Dan
authors.initials: D
authors.lastname: Yang
mesh_terms: D000124:Achievement; D004522:Educational Status; D005544:Forecasting; D006801:Humans; D020407:Internet; D007858:Learning / Q000502:physiology*; D009042:Motivation / Q000145:classification* / Q000502:physiology*