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Predicting student performance in a beginning computer science class

Author:

Laurie Honour WerthAuthors Info & Claims

SIGCSE '86: Proceedings of the seventeenth SIGCSE technical symposium on Computer science education

Pages 138 - 143

https://doi.org/10.1145/5600.5701

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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Cited By

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Delgado Jojoa JRevelo Sánchez OChamorro S(2023)Psychological Models and Instruments Employed to Identify Personality Traits of Software Developers: A Systematic Mapping StudyHuman-Computer Interaction10.1007/978-3-031-24709-5_11(146-161)Online publication date: 22-Jan-2023
https://doi.org/10.1007/978-3-031-24709-5_11
Bockmon RCooper SGratch JZhang JDorodchi MGiannakos MSindre GLuxton-Reilly ADivitini M(2020)Can Students' Spatial Skills Predict Their Programming Abilities?Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3341525.3387380(446-451)Online publication date: 15-Jun-2020
https://dl.acm.org/doi/10.1145/3341525.3387380
Quille KBergin S(2019)CS1: how will they do? How can we help? A decade of research and practiceComputer Science Education10.1080/08993408.2019.161267929:2-3(254-282)Online publication date: 29-May-2019
https://doi.org/10.1080/08993408.2019.1612679
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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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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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Published In

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

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

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

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Publication History

Published: 01 February 1986

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SIGCSE86: SIGSCE Technical symposium on Computer Science Education

February 6 - 7, 1986

Ohio, Cincinnati, USA

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Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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https://doi.org/10.1007/978-3-031-24709-5_11
Bockmon RCooper SGratch JZhang JDorodchi MGiannakos MSindre GLuxton-Reilly ADivitini M(2020)Can Students' Spatial Skills Predict Their Programming Abilities?Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3341525.3387380(446-451)Online publication date: 15-Jun-2020
https://dl.acm.org/doi/10.1145/3341525.3387380
Quille KBergin S(2019)CS1: how will they do? How can we help? A decade of research and practiceComputer Science Education10.1080/08993408.2019.161267929:2-3(254-282)Online publication date: 29-May-2019
https://doi.org/10.1080/08993408.2019.1612679
Hellas AIhantola PPetersen AAjanovski VGutica MHynninen TKnutas ALeinonen JMessom CLiao SPolycarpou IRead JAndreou PArmoni M(2018)Taxonomizing features and methods for identifying at-risk students in computing coursesProceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education10.1145/3197091.3205845(364-365)Online publication date: 2-Jul-2018
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Parappilly MHassam CWoodman R(2018)Race to improve student understanding of uncertainty: Using LEGO race cars in the physics labAmerican Journal of Physics10.1119/1.500381286:1(68-76)Online publication date: 1-Jan-2018
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Ahadi AHellas ALister R(2017)A Contingency Table Derived Method for Analyzing Course DataACM Transactions on Computing Education10.1145/312381417:3(1-19)Online publication date: 28-Aug-2017
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Ahadi ALister RLal SLeinonen JHellas A(2017)Performance and Consistency in Learning to ProgramProceedings of the Nineteenth Australasian Computing Education Conference10.1145/3013499.3013503(11-16)Online publication date: 31-Jan-2017
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Ahadi ALister RHaapala HVihavainen ADorn BSheard JCutts Q(2015)Exploring Machine Learning Methods to Automatically Identify Students in Need of AssistanceProceedings of the eleventh annual International Conference on International Computing Education Research10.1145/2787622.2787717(121-130)Online publication date: 9-Jul-2015
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Kranch DDougherty JNagel KDecker AEiselt K(2014)Remediation and student success in CIS programsProceedings of the 45th ACM technical symposium on Computer science education10.1145/2538862.2538962(689-694)Online publication date: 5-Mar-2014
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Fabros-Tyler G(2014)English, Mathematics, and Programming grades in the secondary level as predictors of academic performance in the college levelIISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications10.1109/IISA.2014.6878739(427-431)Online publication date: Jul-2014
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