Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment

@inproceedings{Wolff2013ImprovingRP,
  title={Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment},
  author={Annika Wolff and Zdenek Zdr{\'a}hal and Andriy Nikolov and Michal Pantucek},
  booktitle={LAK},
  year={2013}
}
One of the key interests for learning analytics is how it can be used to improve retention. This paper focuses on work conducted at the Open University (OU) into predicting students who are at risk of failing their module. The Open University is one of the worlds largest distance learning institutions. Since tutors do not interact face to face with students, it can be difficult for tutors to identify and respond to students who are struggling in time to try to resolve the difficulty. Predictive… CONTINUE READING
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