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Predicting performance in an introductory computer science course

Authors:

D. F. Butcher,

W. A. MuthAuthors Info & Claims

Communications of the ACM, Volume 28, Issue 3

Pages 263 - 268

https://doi.org/10.1145/3166.3167

Published: 01 March 1985 Publication History

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Abstract

A group of 269 first-semester freshmen was used to predict both performance in an introductory computer science course and first-semester college grade point average by using information regarding the students' programs and performance in high school along with American College Testing Program (ACT) test scores.

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Quille KVidal-Meliá LNolan KMooney A(2023)Evolving Towards a Trustworthy AIEd Model to Predict at Risk Students in Introductory Programming CoursesProceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice10.1145/3633083.3633190(22-28)Online publication date: 14-Dec-2023
https://dl.acm.org/doi/10.1145/3633083.3633190
Apiola MSaqr MLópez-Pernas S(2023)The Evolving Themes of Computing Education Research: Trends, Topic Models, and Emerging ResearchPast, Present and Future of Computing Education Research10.1007/978-3-031-25336-2_8(151-169)Online publication date: 5-Jan-2023
https://doi.org/10.1007/978-3-031-25336-2_8
Kiser SAndrews CSeidel SFisher MWright NTheobald E(2022)Increased Pass Rates in Introductory Biology: Benefits and Potential Costs of Implementing a Mathematics Prerequisite in a Community College SettingCBE—Life Sciences Education10.1187/cbe.21-09-024821:4Online publication date: Dec-2022
https://doi.org/10.1187/cbe.21-09-0248
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Index Terms

Predicting performance in an introductory computer science course
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Accreditation
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types

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Published In

Communications of the ACM Volume 28, Issue 3

March 1985

94 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3166

Editor:
Peter J. Denning
NASA Ames Research Center, Moffett Field, CA

Issue’s Table of Contents

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 1985

Published in CACM Volume 28, Issue 3

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Quille KVidal-Meliá LNolan KMooney A(2023)Evolving Towards a Trustworthy AIEd Model to Predict at Risk Students in Introductory Programming CoursesProceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice10.1145/3633083.3633190(22-28)Online publication date: 14-Dec-2023
https://dl.acm.org/doi/10.1145/3633083.3633190
Apiola MSaqr MLópez-Pernas S(2023)The Evolving Themes of Computing Education Research: Trends, Topic Models, and Emerging ResearchPast, Present and Future of Computing Education Research10.1007/978-3-031-25336-2_8(151-169)Online publication date: 5-Jan-2023
https://doi.org/10.1007/978-3-031-25336-2_8
Kiser SAndrews CSeidel SFisher MWright NTheobald E(2022)Increased Pass Rates in Introductory Biology: Benefits and Potential Costs of Implementing a Mathematics Prerequisite in a Community College SettingCBE—Life Sciences Education10.1187/cbe.21-09-024821:4Online publication date: Dec-2022
https://doi.org/10.1187/cbe.21-09-0248
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Williams LTitus KPittman J(2021)How Early is Early Enough: Correlating Student Performance with Final Grades.Proceedings of the 5th Conference on Computing Education Practice10.1145/3437914.3437972(13-16)Online publication date: 7-Jan-2021
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Abstract

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Cited By

Index Terms

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Predicting student performance in a beginning computer science class

Student attitudes toward statistics before and after a computer-integrated introductory statistics course

Re-designing a university introductory computing course using computer-based instruction and assessment

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