research-article

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

Authors:

Zdenek Zdrahal,

Andriy Nikolov,

Michal PantucekAuthors Info & Claims

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

Pages 145 - 149

https://doi.org/10.1145/2460296.2460324

Published: 08 April 2013 Publication History

Abstract

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 models have been developed and tested using historic Virtual Learning Environment (VLE) activity data combined with other data sources, for three OU modules. This has revealed that it is possible to predict student failure by looking for changes in user's activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour. More focused analysis of these modules applying the GUHA (General Unary Hypothesis Automaton) method of data analysis has also yielded some early promising results for creating accurate hypothesis about students who fail.

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Basha SRao AAmmannamma T(2025)Transforming Education With Predictive AnalyticsDriving Quality Education Through AI and Data Science10.4018/979-8-3693-8292-9.ch019(433-456)Online publication date: 28-Feb-2025
https://doi.org/10.4018/979-8-3693-8292-9.ch019
Sipahioglu M(2024)Empowering Teachers With Generative AI Tools and SupportTransforming Education With Generative AI10.4018/979-8-3693-1351-0.ch011(214-238)Online publication date: 29-Mar-2024
https://doi.org/10.4018/979-8-3693-1351-0.ch011
Hassan TEdmison BWilliams DCox II LLouvet MKnijnenburg BMcCrickard D(2024)Simplify, Consolidate, Intervene: Facilitating Institutional Support with Mental Models of Learning Management System UseProceedings of the ACM on Human-Computer Interaction10.1145/36870518:CSCW2(1-23)Online publication date: 8-Nov-2024
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Index Terms

Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment
1. Information systems
  1. Information systems applications
    1. Data mining
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
      1. Software development methods

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Published In

cover image ACM Conferences

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

April 2013

300 pages

ISBN:9781450317856

DOI:10.1145/2460296

Editors:
Dan Suthers
University of Hawaii, USA
,
Katrien Verbert
Eindhoven University of Technology, The Netherlands
,
Erik Duval
University of Leuven, Belgium
,
Xavier Ochoa
Escuela Superior Politécnica del Litoral, Ecuador

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
K.U. Leuven: K.U. Leuven
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 08 April 2013

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Joint Information Systems Committee

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LAK '13

Sponsor:

SIGWEB
K.U. Leuven
SIGCHI

LAK '13: Third Conference on Learning Analytics and Knowledge

April 8 - 13, 2013

Leuven, Belgium

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LAK '13 Paper Acceptance Rate 16 of 58 submissions, 28%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

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https://doi.org/10.4018/979-8-3693-8292-9.ch019
Sipahioglu M(2024)Empowering Teachers With Generative AI Tools and SupportTransforming Education With Generative AI10.4018/979-8-3693-1351-0.ch011(214-238)Online publication date: 29-Mar-2024
https://doi.org/10.4018/979-8-3693-1351-0.ch011
Hassan TEdmison BWilliams DCox II LLouvet MKnijnenburg BMcCrickard D(2024)Simplify, Consolidate, Intervene: Facilitating Institutional Support with Mental Models of Learning Management System UseProceedings of the ACM on Human-Computer Interaction10.1145/36870518:CSCW2(1-23)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687051
Tretow-Fish TAndersen JKhalid M(2024)Prototyping an Adaptive Learning Platform's Learning Analytic Dashboards on Behavioral Data to Support Teachers' Pedagogical Actions2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS)10.1109/iCACCESS61735.2024.10499493(1-6)Online publication date: 8-Mar-2024
https://doi.org/10.1109/iCACCESS61735.2024.10499493
Bigler DManz JLee KFischer DHagel G(2024)Learning Environment Interoperability in Software Engineering Education2024 36th International Conference on Software Engineering Education and Training (CSEE&T)10.1109/CSEET62301.2024.10663056(1-5)Online publication date: 29-Jul-2024
https://doi.org/10.1109/CSEET62301.2024.10663056
Rienties BDomingue JDuttaroy SHerodotou CTessarolo FWhitelock D(2024)What distance learning students want from an AI Digital AssistantDistance Education10.1080/01587919.2024.2338717(1-17)Online publication date: 28-May-2024
https://doi.org/10.1080/01587919.2024.2338717
Gray S(2024)Die Ethik der KI in Universitäten: Im Spannungsfeld zwischen Qualität, Identität und PrivatsphäreVertrauen in Künstliche Intelligenz10.1007/978-3-658-43816-6_7(117-135)Online publication date: 19-Sep-2024
https://doi.org/10.1007/978-3-658-43816-6_7
Stadler MBrandl LGreiff S(2023)20 years of interactive tasks in large‐scale assessments: Process data as a way towards sustainable change?Journal of Computer Assisted Learning10.1111/jcal.1284739:6(1852-1859)Online publication date: 3-Jul-2023
https://doi.org/10.1111/jcal.12847
Gelorme Silveira MDe Souza LDe Oliveira Brandão LFranco Brandão A(2023)Learning Analytics to Support Education for All: Learning from the Past2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343491(1-8)Online publication date: 18-Oct-2023
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Pek ROzyer SElhage TOzyer TAlhajj R(2023)The Role of Machine Learning in Identifying Students At-Risk and Minimizing FailureIEEE Access10.1109/ACCESS.2022.323298411(1224-1243)Online publication date: 2023
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