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Predicting Learners’ Behaviours to Get It Wrong

  • Niels Heller
  • François Bry
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 804)

Abstract

One of the most vexing aspects of tertiary education is the learning behaviour of many beginners: Late drop-outs after much time has already been invested in attending a course, incomplete homework even though completed homework is a sufficient condition for success at examinations, and misconceptions that are not overcome early enough. This article presents three predictors related to these learning-impairing behaviours that have been built from data collected with a learning platform and by examining homework assignments, and developed as Hidden Markov Model, by relying on Collaborative Filtering, and by using Multiple Linear Regression. The sensitivities and specificities of the first two predictors are above 70% and the \(R^2\)-error of the third predictor is about 20%. Considering the large numbers of unknown parameters like course-independent learning, this quality is satisfying. The predictors have been developed for fostering a better learning by raising the learners’ consciousness of the deficiencies of their learning. In other words, the predictors aim at “getting it wrong”. The article reports on the predictors and their evaluation.

Keywords

Learning analytics Behaviour prediction User modelling and adaptation in TEL Evidence-based studies 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute for InformaticsLudwig-Maximilian University of MunichMunichGermany

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