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Predicting student performance in a beginning computer science class

Author:

Laurie Honour WerthAuthors Info & Claims

ACM SIGCSE Bulletin, Volume 18, Issue 1

Pages 138 - 143

https://doi.org/10.1145/953055.5701

Published: 01 February 1986 Publication History

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Abstract

This study investigated the relationship between the student's grade in a beginning computer science course and their sex, age, high school and college academic performance, number of mathematics courses, and work experience. Standard measures of cognitive development, cognitive style, and personality factors were also given to 58 students in three sections of the beginning Pascal programming class.

Significant relationships were found between the letter grade and the students' college grades, the number of hours worked and the number of high school mathematics classes. Both the Group Embedded Figures Test (GEFT) and the measure of Piagetian intellectual development stages were also significantly correlated with grade in the course. There was no relationship between grade and the personality type, as measured by the Myers-Briggs Type Indicator (MBTI); however, an interesting and distinctive personality profile was evident.

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Albinsson PLif P(2024) Attention To Detail and Cyber Skill: Associated Beyond General Intelligence in Cyber‐Soldier Conscripts International Journal of Selection and Assessment10.1111/ijsa.1251733:1Online publication date: 23-Dec-2024
https://doi.org/10.1111/ijsa.12517
Dagklis ESatratzemi MKoloniari GKarakasidis A(2024)Programming Errors and Academic Performance in an Introductory Data Structures Course: A Per Gender AnalysisTowards a Hybrid, Flexible and Socially Engaged Higher Education10.1007/978-3-031-53382-2_6(57-68)Online publication date: 26-Feb-2024
https://doi.org/10.1007/978-3-031-53382-2_6
Neda BWang MBarkam HWong-Ma JGago-Masague S(2023)Revisiting Academic Probation in CS: At-Risk Indicators and Impact on Student SuccessJournal of Computing Sciences in Colleges10.5555/3606442.360644338:10(8-16)Online publication date: 1-Apr-2023
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Index Terms

Predicting student performance in a beginning computer science class
1. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
2. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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Predicting student performance in a beginning computer science class
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Reviews

Reviewer: Lee Girard Herberts

The paper describes a research study on predictors of computer science class performance. Predictors evaluated included background (sex, age, high school and college grades, classification, hours worked, etc.), cognitive (embedded figures), intellectual development (Piaget), and personality (Myers-Briggs [1]), variables. College grades, high school mathematics, embedded figures, and intellectual development were statistically significant factors (p<.05). The author has provided a reasonably comprehensive attack on the “predictor problem” in computer science (including a good reference list). However, the lack of predictors that explain a large proportion of the variance in performance remains. For example, the embedded figures test correlated only .317 with letter grade in the computer class. Also, there are some concerns as to the statistical validity of using Pearson's R, changing the alpha value, and controlling for separate classes. Finally, this research does provide a unique contribution in the analysis of personality predictors (using Myers-Briggs).

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Information & Contributors

Information

Published In

ACM SIGCSE Bulletin Volume 18, Issue 1

Proceedings of the 17th SIGCSE symposium on Computer science education

February 1986

304 pages

ISSN:0097-8418

DOI:10.1145/953055

Issue’s Table of Contents

SIGCSE '86: Proceedings of the seventeenth SIGCSE technical symposium on Computer science education
February 1986
336 pages
ISBN:0897911784
DOI:10.1145/5600
Editors:
Joyce Currie Little
Towson State Univ., Baltimore, MD
,
Lillian N. Cassel
Univ. of Delaware, Newark

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 February 1986

Published in SIGCSE Volume 18, Issue 1

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https://doi.org/10.1111/ijsa.12517
Dagklis ESatratzemi MKoloniari GKarakasidis A(2024)Programming Errors and Academic Performance in an Introductory Data Structures Course: A Per Gender AnalysisTowards a Hybrid, Flexible and Socially Engaged Higher Education10.1007/978-3-031-53382-2_6(57-68)Online publication date: 26-Feb-2024
https://doi.org/10.1007/978-3-031-53382-2_6
Neda BWang MBarkam HWong-Ma JGago-Masague S(2023)Revisiting Academic Probation in CS: At-Risk Indicators and Impact on Student SuccessJournal of Computing Sciences in Colleges10.5555/3606442.360644338:10(8-16)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.5555/3606442.3606443
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