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Coenrollment networks and their relationship to grades in undergraduate education

Authors:

Christopher BrooksAuthors Info & Claims

LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge

Pages 295 - 304

https://doi.org/10.1145/3170358.3170373

Published: 07 March 2018 Publication History

Abstract

In this paper, we evaluate the complete undergraduate coenrollment network over a decade of education at a large American public university. We provide descriptive properties of the network, demonstrating that the coenrollment networks evaluated follow power-law degree distributions similar to many other large-scale networks; that they reveal strong performance-based assortativity; and that network-based features can significantly improve GPA-based student performance predictors. We then implement a network-based, multi-view classification model to predict students' final course grades. In particular, we adapt a structural modeling approach from [19, 34], whereby we model the university-wide undergraduate coenrollment network as an undirected graph. We compare the performance of our predictor to traditional methods used for grade prediction in undergraduate university courses, and demonstrate that a multi-view ensembling approach outperforms both prior "flat" and network-based models for grade prediction across several classification metrics. These findings demonstrate the usefulness of combining diverse approaches in models of student success, and demonstrate specific network-based modeling strategies which are likely to be most effective for grade prediction.

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Cited By

Poquet ONguyen QKovanovic VBrooks CDawson SBiotteau A(2022)Grade-based similarity prevails in online course forums at scaleComputers & Education10.1016/j.compedu.2021.104401178:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.compedu.2021.104401
Poquet OScheffel MDowell NJoksimovic SSiemens G(2021)Why Birds of a Feather Flock Together: Factors Triaging Students in Online ForumsLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448185(469-474)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448185
Fincham ERozemberczki BKovanovic VJoksimovic SJovanovic JGasevic D(2021)Persistence and Performance in Co-Enrollment Network Embeddings: An Empirical Validation of Tinto's Student Integration ModelIEEE Transactions on Learning Technologies10.1109/TLT.2021.305936214:1(106-121)Online publication date: Feb-2021
https://doi.org/10.1109/TLT.2021.3059362
Show More Cited By

Index Terms

Coenrollment networks and their relationship to grades in undergraduate education
1. Applied computing
  1. Education
    1. Computer-assisted instruction

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Published In

cover image ACM Other conferences

LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge

March 2018

489 pages

ISBN:9781450364003

DOI:10.1145/3170358

Conference Chairs:
Abelardo Pardo
The University of Sydney, AUS
,
Kathryn Bartimote-Aufflick
The University of Sydney, AUS
,
Grace Lynch
Society for Learning Analytics Research, AUS
,
Program Chairs:
Simon Buckingham Shum
University of Technology Sydney, AUS
,
Rebecca Ferguson
The Open University, UK
,
Agathe Merceron
Beuth University of Applied Sciences, GER
,
Xavier Ochoa
Escuela Superior Politécnica del Litoral, EC

Copyright © 2018 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 the author(s) 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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Published: 07 March 2018

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Michigan Institute for Data Science (MIDAS)

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LAK '18

LAK '18: International Conference on Learning Analytics and Knowledge

March 7 - 9, 2018

New South Wales, Sydney, Australia

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LAK '18 Paper Acceptance Rate 35 of 115 submissions, 30%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Poquet ONguyen QKovanovic VBrooks CDawson SBiotteau A(2022)Grade-based similarity prevails in online course forums at scaleComputers & Education10.1016/j.compedu.2021.104401178:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.compedu.2021.104401
Poquet OScheffel MDowell NJoksimovic SSiemens G(2021)Why Birds of a Feather Flock Together: Factors Triaging Students in Online ForumsLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448185(469-474)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448185
Fincham ERozemberczki BKovanovic VJoksimovic SJovanovic JGasevic D(2021)Persistence and Performance in Co-Enrollment Network Embeddings: An Empirical Validation of Tinto's Student Integration ModelIEEE Transactions on Learning Technologies10.1109/TLT.2021.305936214:1(106-121)Online publication date: Feb-2021
https://doi.org/10.1109/TLT.2021.3059362
Dowell NPoquet O(2021)SCIPComputers in Human Behavior10.1016/j.chb.2021.106709119:COnline publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1016/j.chb.2021.106709
Poquet OJovanovic JRensing CDrachsler HKovanović VPinkwart NScheffel MVerbert K(2020)Intergroup and interpersonal forum positioning in shared-thread and post-reply networksProceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375533(187-196)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3375462.3375533
Jiang WPardos ZWei QHsiao SCunningham JMcCarthy KLynch GBrooks CFerguson RHoppe U(2019)Goal-based Course RecommendationProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303814(36-45)Online publication date: 4-Mar-2019
https://dl.acm.org/doi/10.1145/3303772.3303814
Li WBrooks CSchaub FHsiao SCunningham JMcCarthy KLynch GBrooks CFerguson RHoppe U(2019)The Impact of Student Opt-Out on Educational Predictive ModelsProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303809(411-420)Online publication date: 4-Mar-2019
https://dl.acm.org/doi/10.1145/3303772.3303809
Nur NPark NDorodchi MDou WMahzoon MNiu XMaher M(2019)Student Network Analysis: A Novel Way to Predict Delayed Graduation in Higher EducationArtificial Intelligence in Education10.1007/978-3-030-23204-7_31(370-382)Online publication date: 21-Jun-2019
https://doi.org/10.1007/978-3-030-23204-7_31

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