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A supervised learning framework: using assessment to identify students at risk of dropping out of a MOOC

  • David Monllaó OlivéEmail author
  • Du Q. Huynh
  • Mark Reynolds
  • Martin Dougiamas
  • Damyon Wiese
Article

Abstract

Both educational data mining and learning analytics aim to understand learners and optimise learning processes of educational settings like Moodle, a learning management system (LMS). Analytics in an LMS covers many different aspects: finding students at risk of abandoning a course or identifying students with difficulties before the assessments. Thus, there are multiple prediction models that can be explored. The prediction models can target at the course also. For instance, will this activity assessment engage learners? To ease the evaluation and usage of prediction models in Moodle, we abstract out the most relevant elements of prediction models and develop an analytics framework for Moodle. Apart from the software framework, we also present a case study model which uses variables based on assessments to predict students at risk of dropping out of a massive open online course that has been offered eight times from 2013 to 2018, including a total of 46,895 students. A neural network is trained with data from past courses and the framework generates insights about students at risk in ongoing courses. Predictions are then generated after the first, the second, and the third quarters of the course. The average accuracy that we achieve is 88.81% with a 0.9337 F1 score and a 73.12% of the area under the ROC curve.

Keywords

Assessment Learning management systems Moodle Learning analytics Educational data mining Machine learning Neural networks 

Notes

Acknowledgements

This research project was funded by Moodle Pty Ltd, and by the Australian government and The University of Western Australia through the Research Training Program (RTP). We thank Moodle HQ for providing the dataset used in this study. Special thanks for Helen Foster and Mary Cooch for setting up the MOOC and for running regular versions of the course. Also thanks to all Moodle HQ staff and members of the Moodle community that participated in the project by doing code reviews, by testing the framework and by helping design the user interface of the tool.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The University of Western AustraliaPerthAustralia
  2. 2.Moodle Pty Ltd.West PerthAustralia

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