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Using Educational Analytics to Improve Test Performance

  • Owen CorriganEmail author
  • Alan F. Smeaton
  • Mark Glynn
  • Sinéad Smyth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9307)

Abstract

Learning analytics are being used in many educational applications in order to help students and Faculty. In our work we use predictive analytics, using student behaviour to predict the likely performance of end of semester final grades with a system we call PredictED. The main contribution of our approach is that our intervention automatically emailed students on a regular basis, with our prediction for the outcome of their exam performance. We targeted first year, first semester University students who often struggle with making the transition into University life where they are given much more responsibility for things like attending class, completing assignments, etc. The form of student behaviour that we used is students’ levels and types of engagement with the University’s Virtual Learning Environment (VLE), Moodle. We mined the Moodle access log files for a range of parameters based on temporal as well as content access, and use machine learning techniques to predict likely pass/fail, on a weekly basis throughout the semester using logs and outcomes from previous years as training material. We chose ten first-year modules with reasonably high failure rates, large enrolments and stability of module content across the years to implement an early warning system on. From these modules 1,558 students were registered for one of these modules. They were offered the chance to opt into receiving weekly email alerts warning them about their likely outcome. Of these 75 % or 1,181 students opted into this service. Pre-intervention there were no differences between participants and non-participants on a number of measures related to previous academic record. However, post-intervention the first-attempt final grade performance yielded nearly 3 % improvement (58.4 % to 61.2 %) on average for those who opted in. This tells us that providing weekly guidance and personalised feedback to vulnerable first year students, automatically generated from monitoring of their online behaviour, has a significant positive effect on their exam performance.

Keywords

Learning analytics Mining educational data Predictive analytics Machine learning 

Notes

Acknowledgements

This research was supported by Science Foundation Ireland under grant number SFI/12/RC/2289, and by Dublin City University. The authors wish to thank Aisling McKenna for her help with the statistical analysis of some of the results.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Owen Corrigan
    • 1
    Email author
  • Alan F. Smeaton
    • 1
  • Mark Glynn
    • 2
  • Sinéad Smyth
    • 3
  1. 1.Insight Centre for Data AnalyticsDublin City UniversityDublin 9Ireland
  2. 2.Teaching Enhancement UnitDublin City UniversityDublin 9Ireland
  3. 3.School of Nursing and Human SciencesDublin City UniversityDublin 9Ireland

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