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An Improved Grade Point Average, With Applications to CS Undergraduate Education Analytics

Authors:

Jonathan H. Tomkin,

Geoffrey L. HermanAuthors Info & Claims

ACM Transactions on Computing Education (TOCE), Volume 18, Issue 4

Article No.: 17, Pages 1 - 16

https://doi.org/10.1145/3157086

Published: 13 September 2018 Publication History

Abstract

We present a methodological improvement for calculating Grade Point Averages (GPAs). Heterogeneity in grading between courses systematically biases observed GPAs for individual students: the GPA observed depends on course selection. We show how a logistic model can account for course selection by simulating how every student in a sample would perform if they took all available courses, giving a new “modeled GPA.” We then use 10 years of grade data from a large university to demonstrate that this modeled GPA is a more accurate predictor of student performance in individual courses than the observed GPA. Using Computer Science (CS) as an example learning analytics application, it is found that required CS courses give significantly lower grades than average courses. This depresses the recorded GPAs of CS majors: modeled GPAs are 0.25 points higher than those that are observed. The modeled GPA also correlates much more closely with standardized test scores than the observed GPA: the correlation with Math ACT is 0.37 for the modeled GPA and is 0.20 for the observed GPA. This implies that standardized test scores are much better predictors of student performance than might otherwise be assumed.

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Cited By

Abou Naaj MMehdi RMohamed ENachouki M(2023)Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy ApproachEducation Sciences10.3390/educsci1303031313:3(313)Online publication date: 17-Mar-2023
https://doi.org/10.3390/educsci13030313
Guevara-Flores KHernández-Calderón JSoto-Mendoza V(2023)Enhancing English Proficiency Test Evaluation: Leveraging Artificial Intelligence for Result Classification2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI)10.1109/ISCMI59957.2023.10458530(183-187)Online publication date: 25-Nov-2023
https://doi.org/10.1109/ISCMI59957.2023.10458530
Mehdi RNachouki M(2023)Genetic Algorithm-Based Approach for Predicting Student Academic Success2023 24th International Arab Conference on Information Technology (ACIT)10.1109/ACIT58888.2023.10453789(1-5)Online publication date: 6-Dec-2023
https://doi.org/10.1109/ACIT58888.2023.10453789
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Index Terms

An Improved Grade Point Average, With Applications to CS Undergraduate Education Analytics
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer engineering education
        Computer science education
        CS1
  2. User characteristics
    1. Gender

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Information & Contributors

Information

Published In

cover image ACM Transactions on Computing Education

ACM Transactions on Computing Education Volume 18, Issue 4

Special Issue on Learning Analytics and Regular Papers

December 2018

118 pages

EISSN:1946-6226

DOI:10.1145/3278118

Editor:
Chris Hundhausen
Washington State University, USA

Issue’s Table of Contents

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2018

Accepted: 01 October 2017

Revised: 01 April 2017

Received: 01 September 2016

Published in TOCE Volume 18, Issue 4

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Citations

Cited By

Abou Naaj MMehdi RMohamed ENachouki M(2023)Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy ApproachEducation Sciences10.3390/educsci1303031313:3(313)Online publication date: 17-Mar-2023
https://doi.org/10.3390/educsci13030313
Guevara-Flores KHernández-Calderón JSoto-Mendoza V(2023)Enhancing English Proficiency Test Evaluation: Leveraging Artificial Intelligence for Result Classification2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI)10.1109/ISCMI59957.2023.10458530(183-187)Online publication date: 25-Nov-2023
https://doi.org/10.1109/ISCMI59957.2023.10458530
Mehdi RNachouki M(2023)Genetic Algorithm-Based Approach for Predicting Student Academic Success2023 24th International Arab Conference on Information Technology (ACIT)10.1109/ACIT58888.2023.10453789(1-5)Online publication date: 6-Dec-2023
https://doi.org/10.1109/ACIT58888.2023.10453789
Mehdi RNachouki M(2023)A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programsEducation and Information Technologies10.1007/s10639-022-11205-228:3(2455-2484)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10639-022-11205-2
Malmi LSheard JKinnunen PSimon Sinclair J(2022)Development and Use of Domain-specific Learning Theories, Models, and Instruments in Computing EducationACM Transactions on Computing Education10.1145/353022123:1(1-48)Online publication date: 29-Dec-2022
https://dl.acm.org/doi/10.1145/3530221
Nachouki MMehdi R(2021)Particle Swarm Model for Predicting Student Performance in Computing ProgramsIntelligent Systems and Applications10.1007/978-3-030-82196-8_7(84-96)Online publication date: 3-Aug-2021
https://doi.org/10.1007/978-3-030-82196-8_7
Poulsen SGiannakos MSindre GLuxton-Reilly ADivitini M(2020)Using Spatio-Algorithmic Problem Solving Strategies to Increase Access to Data StructuresProceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3341525.3394004(577-578)Online publication date: 15-Jun-2020
https://dl.acm.org/doi/10.1145/3341525.3394004
Ramli IMaat SKhalid F(2019)Learning Analytics in Mathematics: A Systematic ReviewInternational Journal of Academic Research in Progressive Education and Development10.6007/IJARPED/v8-i4/65638:4Online publication date: 4-Dec-2019
https://doi.org/10.6007/IJARPED/v8-i4/6563

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