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Modest analytics: using the index method to identify students at risk of failure

Authors:

Cassandra Colvin,

Belinda ChieraAuthors Info & Claims

LAK '14: Proceedings of the Fourth International Conference on Learning Analytics And Knowledge

Pages 118 - 122

https://doi.org/10.1145/2567574.2567629

Published: 24 March 2014 Publication History

Abstract

Regression is the tool of choice for developing predictive models of student risk of failure. However, the forecasting literature has demonstrated the predictive equivalence of much simpler methods. We directly compare one simple tabulation technique, the index method, to a linear multiple regression approach for identifying students at risk. The broader purpose is to explore the plausibility of a flexible method that is conducive to adoption and diffusion. In this respect this paper fits within the ambit of the modest computing agenda, and suggests the possibility of a modest analytics. We built both regression and index method models on 2011 student data and applied these to 2012 student data. The index method was comparable in terms of predictive accuracy of student risk. We suggest that the context specificity of learning environments makes the index method a promising tool for educators who want a situated risk algorithm that is flexible and adaptable.

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Cited By

McConvey KGuha SKuzminykh A(2023)A Human-Centered Review of Algorithms in Decision-Making in Higher EducationProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580658(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580658
Ahmad ASchneider JGriffiths DBiedermann DSchiffner DGreller WDrachsler H(2022)Connecting the dots – A literature review on learning analytics indicators from a learning design perspectiveJournal of Computer Assisted Learning10.1111/jcal.1271640:6(2432-2470)Online publication date: 26-Jul-2022
https://doi.org/10.1111/jcal.12716
Hemmler YIfenthaler D(2022)Indicators of the Learning Context for Supporting Personalized and Adaptive Learning Environments2022 International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT55010.2022.00026(61-65)Online publication date: Jul-2022
https://doi.org/10.1109/ICALT55010.2022.00026
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Index Terms

Modest analytics: using the index method to identify students at risk of failure
1. Mathematics of computing
  1. Probability and statistics

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Published In

cover image ACM Other conferences

LAK '14: Proceedings of the Fourth International Conference on Learning Analytics And Knowledge

March 2014

301 pages

ISBN:9781450326643

DOI:10.1145/2567574

Conference Chairs:
Mattew Pistilli
Purdue University, IN
,
James Willis
Purdue University, IN
,
Drew Koch
John N. Gardner Institute for Excellence in Undergraduate Education, NC
,
Kimberly Arnold
University of Wisconsin-Madison, WI
,
Program Chairs:
Stephanie Teasley
University of Michigan, MI
,
Abelardo Pardo
The University of Sydney, Australia

Copyright © 2014 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]

Sponsors

JNGI: John N. Gardner Institute for Excellence in Undergraduate Education
University of Wisc-Madison: University of Wisconsin-Madison
SoLAR: The Society for Learning Analytics Research
Purdue University: Purdue University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

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LAK '14

Sponsor:

JNGI
University of Wisc-Madison
SoLAR
Purdue University

LAK '14: Learning Analytics and Knowledge Conference 2014

March 24 - 28, 2014

Indiana, Indianapolis, USA

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LAK '14 Paper Acceptance Rate 13 of 44 submissions, 30%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

McConvey KGuha SKuzminykh A(2023)A Human-Centered Review of Algorithms in Decision-Making in Higher EducationProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580658(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580658
Ahmad ASchneider JGriffiths DBiedermann DSchiffner DGreller WDrachsler H(2022)Connecting the dots – A literature review on learning analytics indicators from a learning design perspectiveJournal of Computer Assisted Learning10.1111/jcal.1271640:6(2432-2470)Online publication date: 26-Jul-2022
https://doi.org/10.1111/jcal.12716
Hemmler YIfenthaler D(2022)Indicators of the Learning Context for Supporting Personalized and Adaptive Learning Environments2022 International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT55010.2022.00026(61-65)Online publication date: Jul-2022
https://doi.org/10.1109/ICALT55010.2022.00026
Ifenthaler DYau J(2020)Utilising learning analytics to support study success in higher education: a systematic reviewEducational Technology Research and Development10.1007/s11423-020-09788-z68:4(1961-1990)Online publication date: 12-Jun-2020
https://doi.org/10.1007/s11423-020-09788-z
Hung JShelton BYang JDu X(2019)Improving Predictive Modeling for At-Risk Student Identification: A Multistage ApproachIEEE Transactions on Learning Technologies10.1109/TLT.2019.291107212:2(148-157)Online publication date: 1-Apr-2019
https://doi.org/10.1109/TLT.2019.2911072
Hellas AIhantola PPetersen AAjanovski VGutica MHynninen TKnutas ALeinonen JMessom CLiao SRößling GScharlau B(2018)Predicting academic performance: a systematic literature reviewProceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education10.1145/3293881.3295783(175-199)Online publication date: 2-Jul-2018
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Biedermann DSchneider JDrachsler H(2018)The Learning Analytics Indicator RepositoryLifelong Technology-Enhanced Learning10.1007/978-3-319-98572-5_49(579-582)Online publication date: 14-Aug-2018
https://doi.org/10.1007/978-3-319-98572-5_49
Schaffer HYoung KLigon EChapman D(2017)Automating Individualized Formative Feedback in Large Classes Based on a Directed Concept GraphFrontiers in Psychology10.3389/fpsyg.2017.002608Online publication date: 28-Feb-2017
https://doi.org/10.3389/fpsyg.2017.00260
al-Rifaie MYee-King Md'Inverno M(2017)Boolean prediction of final grades based on weekly and cumulative activities2017 Intelligent Systems Conference (IntelliSys)10.1109/IntelliSys.2017.8324334(462-469)Online publication date: Sep-2017
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Magdalene Delighta Angeline DRamasubramanian PJames I(2017)Predicting Academic Performance in Teaching Learning Scheme Using Data Mining Practice2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)10.1109/ICCIC.2017.8524585(1-5)Online publication date: Dec-2017
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