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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 2174613, 7 pages
http://dx.doi.org/10.1155/2016/2174613
Research Article

Motivation Classification and Grade Prediction for MOOCs Learners

1Computing Center, Northeastern University, Shenyang 110819, China
2College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Received 5 September 2015; Revised 20 November 2015; Accepted 23 November 2015

Academic Editor: Elio Masciari

Copyright © 2016 Bin Xu and Dan Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.