research-article

What and when: the role of course type and timing in students' academic performance

Authors:

Michael Geoffrey Brown,

R. Matthew DeMonbrun,

Stephen J. Aguilar,

Stephanie D. TeasleyAuthors Info & Claims

LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge

Pages 459 - 468

https://doi.org/10.1145/2883851.2883907

Published: 25 April 2016 Publication History

Abstract

In this paper we discuss the results of a study of students' academic performance in first year general education courses. Using data from 566 students who received intensive academic advising as part of their enrollment in the institution's pre-major/general education program, we investigate individual student, organizational, and disciplinary factors that might predict a students' potential classification in an Early Warning System as well as factors that predict improvement and decline in their academic performance. Disciplinary course type (based on Biglan's [7] typology) was significantly related to a student's likelihood to enter below average performance classifications. Students were the most likely to enter a classification in fields like the natural science, mathematics, and engineering in comparison to humanities courses. We attribute these disparities in academic performance to disciplinary norms around teaching and assessment. In particular, the timing of assessments played a major role in students' ability to exit a classification. Implications for the design of Early Warning analytics systems as well as academic course planning in higher education are offered.

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Cited By

Glandorf DLee HOrona GPumptow MYu RFischer C(2024)Temporal and Between-Group Variability in College Dropout PredictionProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636906(486-497)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636906
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
Zhou JBhat SScheffel MDowell NJoksimovic SSiemens G(2021)Modeling Consistency Using Engagement Patterns in Online CoursesLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448161(226-236)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448161
Show More Cited By

Index Terms

What and when: the role of course type and timing in students' academic performance
1. Applied computing
  1. Education
    1. Learning management systems
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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Published In

cover image ACM Other conferences

LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge

April 2016

567 pages

ISBN:9781450341905

DOI:10.1145/2883851

General Chairs:
Dragan Gašević
University of Edinburgh, United Kingdom
,
Grace Lynch
Society for Learning Analytics Research
,
Program Chairs:
Shane Dawson
University of South Australia
,
Hendrik Drachsler
University of the Netherlands
,
Carolyn Penstein Rosé
Carnegie Mellon University

Copyright © 2016 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2016

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LAK '16

LAK '16: 6th International Conference on Learning Analytics and Knowledge

April 25 - 29, 2016

Edinburgh, United Kingdom

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LAK '16 Paper Acceptance Rate 36 of 116 submissions, 31%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Glandorf DLee HOrona GPumptow MYu RFischer C(2024)Temporal and Between-Group Variability in College Dropout PredictionProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636906(486-497)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636906
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
Zhou JBhat SScheffel MDowell NJoksimovic SSiemens G(2021)Modeling Consistency Using Engagement Patterns in Online CoursesLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448161(226-236)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448161
Ott CMills SStanger NJohn SZwanenburg S(2021)Is this Degree for Me?Exploring computing students’ study decisionsProceedings of the 23rd Australasian Computing Education Conference10.1145/3441636.3442310(96-105)Online publication date: 2-Feb-2021
https://dl.acm.org/doi/10.1145/3441636.3442310
Xu ORen YLiang YLiu MLi CLiu L(2021)Construction of Online Distance Learning Risk Prediction Model Based on Data Mining2021 Tenth International Conference of Educational Innovation through Technology (EITT)10.1109/EITT53287.2021.00020(58-62)Online publication date: Dec-2021
https://doi.org/10.1109/EITT53287.2021.00020
Teasley SKay MElkins SHammond J(2021)User-Centered Design for a Student-Facing Dashboard Grounded in Learning TheoryVisualizations and Dashboards for Learning Analytics10.1007/978-3-030-81222-5_9(191-212)Online publication date: 17-Dec-2021
https://doi.org/10.1007/978-3-030-81222-5_9
Yang ZYang JRice KHung JDu X(2020)Using Convolutional Neural Network to Recognize Learning Images for Early Warning of At-Risk StudentsIEEE Transactions on Learning Technologies10.1109/TLT.2020.298825313:3(617-630)Online publication date: 1-Jul-2020
https://doi.org/10.1109/TLT.2020.2988253
Du XYang JHung J(2020)An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk StudentsIEEE Access10.1109/ACCESS.2020.29648458(10110-10122)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2964845
Liz-Domínguez MCaeiro-Rodríguez MLlamas-Nistal MMikic-Fonte F(2019)Systematic Literature Review of Predictive Analysis Tools in Higher EducationApplied Sciences10.3390/app92455699:24(5569)Online publication date: 17-Dec-2019
https://doi.org/10.3390/app9245569
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
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