Motivation Classification and Grade Prediction for MOOCs Learners

Abstract EN

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.