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Customized course advising: investigating engineering student success with incoming profiles and patterns of concurrent course enrollment

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Darryl KochAuthors Info & Claims

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

Pages 16 - 25

https://doi.org/10.1145/2567574.2567589

Published: 24 March 2014 Publication History

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Abstract

Every college student registers for courses from a catalog of numerous offerings each term. Selecting the courses in which to enroll, and in what combinations, can dramatically impact each student's chances for academic success. Taking inspiration from the STEM Academy, we wanted to identify the characteristics of engineering students who graduate with 3.0 or above grade point average. The overall goal of the Customized Course Advising project is to determine the optimal term-by-term course selections for all engineering students based on their incoming characteristics and previous course history and performance, paying particular attention to concurrent enrollment. We found that ACT Math, SAT Math, and Advanced Placement exam can be effective measures to measure the students' academic preparation level. Also, we found that some concurrent course-enrollment patterns are highly predictive of first-term and overall academic success.

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Cited By

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McCarren EAdams GMcNeilly J(2024)What Makes a Grade? Harnessing Demographic and Supportive Factors to Predict General Chemistry I CompletionJournal of Chemical Education10.1021/acs.jchemed.4c00042101:6(2279-2289)Online publication date: 14-May-2024
https://doi.org/10.1021/acs.jchemed.4c00042
Aguilar S(2023)Using Motivation Theory to Design Equity-Focused Learning Analytics DashboardsTrends in Higher Education10.3390/higheredu20200152:2(283-290)Online publication date: 29-Mar-2023
https://doi.org/10.3390/higheredu2020015
Li CHerbert NYeom SMontgomery J(2022)Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic ReviewEducation Sciences10.3390/educsci1211078112:11(781)Online publication date: 3-Nov-2022
https://doi.org/10.3390/educsci12110781
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Index Terms

Customized course advising: investigating engineering student success with incoming profiles and patterns of concurrent course enrollment
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Information systems applications
    1. Decision support systems

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Information & Contributors

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Published In

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

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: 24 March 2014

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University of Michigan

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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

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McCarren EAdams GMcNeilly J(2024)What Makes a Grade? Harnessing Demographic and Supportive Factors to Predict General Chemistry I CompletionJournal of Chemical Education10.1021/acs.jchemed.4c00042101:6(2279-2289)Online publication date: 14-May-2024
https://doi.org/10.1021/acs.jchemed.4c00042
Aguilar S(2023)Using Motivation Theory to Design Equity-Focused Learning Analytics DashboardsTrends in Higher Education10.3390/higheredu20200152:2(283-290)Online publication date: 29-Mar-2023
https://doi.org/10.3390/higheredu2020015
Li CHerbert NYeom SMontgomery J(2022)Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic ReviewEducation Sciences10.3390/educsci1211078112:11(781)Online publication date: 3-Nov-2022
https://doi.org/10.3390/educsci12110781
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.12716Online publication date: 26-Jul-2022
https://doi.org/10.1111/jcal.12716
Bahr PMcNaughtan JJackson G(2022)Reducing the Loss of Community College Students who Demonstrate Potential in STEMResearch in Higher Education10.1007/s11162-022-09713-864:5(675-704)Online publication date: 9-Nov-2022
https://doi.org/10.1007/s11162-022-09713-8
Peach RGreenbury SJohnston IYaliraki SLefevre DBarahona M(2021)Understanding learner behaviour in online courses with Bayesian modelling and time series characterisationScientific Reports10.1038/s41598-021-81709-311:1Online publication date: 2-Feb-2021
https://doi.org/10.1038/s41598-021-81709-3
Joksimović SMarshall RRakotoarivelo TLadjal DZhan CPardo A(2021)Privacy-Driven Learning AnalyticsManage Your Own Learning Analytics10.1007/978-3-030-86316-6_1(1-22)Online publication date: 5-Dec-2021
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Aguilar SBaek CHsiao SCunningham JMcCarthy KLynch GBrooks CFerguson RHoppe U(2019)Motivated Information Seeking and Graph Comprehension Among College StudentsProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303805(280-289)Online publication date: 4-Mar-2019
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Nam SSamson P(2019)Integrating Students’ Behavioral Signals and Academic Profiles in Early Warning SystemArtificial Intelligence in Education10.1007/978-3-030-23204-7_29(345-357)Online publication date: 21-Jun-2019
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Brown MDeMonbrun RTeasley SPardo ABartimote-Aufflick KLynch GShum SFerguson RMerceron AOchoa X(2018)Conceptualizing co-enrollmentProceedings of the 8th International Conference on Learning Analytics and Knowledge10.1145/3170358.3170366(305-309)Online publication date: 7-Mar-2018
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