Type

Conference Proceedings

Authors

Sinead Smyth
Mark Glynn
Alan F. Smeaton
Owen Corrigan

Subjects

Education

Topics
information technology performance analytics first year students student behaviour data analytics virtual learning environment education test performance

Using educational analytics to improve test performance (2015)

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.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Science and Health: School of Nursing and Human Sciences
Ireland -> Dublin City University -> DCU Faculties and Centres = University Support Services, Central Administration and Strategic Themes: Office of the Vice-President for Learning Innovation (OVPLI)
Ireland -> Dublin City University -> Subject = Social Sciences: Education
Ireland -> Dublin City University -> Subject = Computer Science: Information technology
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics

Full list of authors on original publication

Sinead Smyth, Mark Glynn, Alan F. Smeaton, Owen Corrigan

Experts in our system

1
Sinead Smyth
Dublin City University
Total Publications: 8
 
2
Mark Glynn
Dublin City University
Total Publications: 10
 
3
Alan F. Smeaton
Dublin City University
Total Publications: 492
 
4
Owen Corrigan
Dublin City University
Total Publications: 9