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Predicting performance by university students in introductory computing courses

Authors:

C. K. Capstick,

J. D. Gordon,

A. SalvadoriAuthors Info & Claims

ACM SIGCSE Bulletin, Volume 7, Issue 3

Pages 21 - 29

https://doi.org/10.1145/382216.382483

Published: 01 September 1975 Publication History

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References

[1]

[1] BATEMAN, G. R., "Predicting Performance in a basic computer course," Proceedings of 5th Annual Conference of the American Institute for Decision Sciences, Boston, 1973.

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[2] "Curriculum 68". Comm. ACM, 11, #3, March 1968.

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[3] Manual for Administrating and Scoring the Aptitude Test for Programmer Personnel. (White Plains, New York, IBM Technical Publications Department, 1964).

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[4] MAZLZCK, L. J., "Compatability of Students from different disciplines and semesters." I.F.I.P. 2nd World Conference of Computers in Education, September 1975.

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[5] NEWSTED, P. R., "Grade and ability predictions in an introductory programming course." SIGCSE BULLETIN, Vol. 7, Number 2, June 1975.

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[6] SAVILLE, P., "Psychological Testing Theoretical Background." Conference on Staff Selection for Data Processing. Dept. of Computing Science, Teesside Polytechnic, Teesside, U.K., 1973.

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[7] SIMPSON, D., "Psychological Testing in computing staff selection--a bibliography." Computer Bulletin, 16, #8, August 1972.

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Cited By

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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
Quille KNam Liao SCostelloe ENolan KMooney AShah KBecker BQuille KLaakso MBarendsen ESimon (2022)PreSSProceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 110.1145/3502718.3524755(54-60)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3502718.3524755
Becker BMooney CKumar ARussell S(2021)A Simple, Language-Independent Approach to Identifying Potentially At-Risk Introductory Programming StudentsProceedings of the 23rd Australasian Computing Education Conference10.1145/3441636.3442318(168-175)Online publication date: 2-Feb-2021
https://dl.acm.org/doi/10.1145/3441636.3442318
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Index Terms

Predicting performance by university students in introductory computing courses
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education
        Information science education

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Published In

ACM SIGCSE Bulletin Volume 7, Issue 3

Sept. 1975

71 pages

ISSN:0097-8418

DOI:10.1145/382216

Editor:
Della T. Bonnette
Univ. of SW Louisiana, Lafayette

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Association for Computing Machinery

New York, NY, United States

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Published: 01 September 1975

Published in SIGCSE Volume 7, 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
Quille KNam Liao SCostelloe ENolan KMooney AShah KBecker BQuille KLaakso MBarendsen ESimon (2022)PreSSProceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 110.1145/3502718.3524755(54-60)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3502718.3524755
Becker BMooney CKumar ARussell S(2021)A Simple, Language-Independent Approach to Identifying Potentially At-Risk Introductory Programming StudentsProceedings of the 23rd Australasian Computing Education Conference10.1145/3441636.3442318(168-175)Online publication date: 2-Feb-2021
https://dl.acm.org/doi/10.1145/3441636.3442318
Matafeni GAjoodha R(2020)Using Big Data Analytics to Predict Learner Attrition based on First Year Marks at a South African UniversityAdvances in Science, Technology and Engineering Systems Journal10.25046/aj05051125:5(920-926)Online publication date: 2020
https://doi.org/10.25046/aj0505112
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
Quille KBergin SPolycarpou IRead JAndreou PArmoni M(2018)Programming: predicting student success early in CS1. a re-validation and replication studyProceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education10.1145/3197091.3197101(15-20)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3197091.3197101
Gordon JCapstick CSalvadori A(2016)An Empirical Study of Cobol ProgrammersINFOR: Information Systems and Operational Research10.1080/03155986.1977.1173166815:2(229-241)Online publication date: 25-May-2016
https://doi.org/10.1080/03155986.1977.11731668
Watson CLi FGodwin J(2013)Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming BehaviorProceedings of the 2013 IEEE 13th International Conference on Advanced Learning Technologies10.1109/ICALT.2013.99(319-323)Online publication date: 15-Jul-2013
https://dl.acm.org/doi/10.1109/ICALT.2013.99
Huggard MMc Goldrick C(2013)Self-affirmation and success in undergraduate Computer Science2013 IEEE Frontiers in Education Conference (FIE)10.1109/FIE.2013.6685149(1806-1811)Online publication date: Oct-2013
https://doi.org/10.1109/FIE.2013.6685149
Koubek RLeBold WSalvendy G(2007)Predicting performance in computer programming coursesBehaviour & Information Technology10.1080/014492985089017934:2(113-129)Online publication date: 31-Jan-2007
https://doi.org/10.1080/01449298508901793
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