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Predicting success in a first programming course

Authors:

R. R. Leeper,

J. L. SilverAuthors Info & Claims

ACM SIGCSE Bulletin, Volume 14, Issue 1

Pages 147 - 150

https://doi.org/10.1145/953051.801357

Published: 01 February 1982 Publication History

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Abstract

The results of a study to develop a predictor of success in a first programming course are presented. The predictor is based on data currently available for a substantial number of students and is tailored to the local program. This predictor is developed using data from a group of students who took the course in the fall of 1980. The results obtained by applying the predictor to a second group of students who took the course in the fall of 1981 are compared with these students' actual grades.

References

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Nie, N. H.et al., SPSS Statistical Package for the Social Sciences, McGraw Hill, New York, 1975.

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Clark, C. T. and Schkrade, L. L., Statistical Analysis for Administrative Decisions, South-Western Publishing Company, Cincinnati, 1979.

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Cited By

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Köhler JHidalgo LJara J(2023)Predicting Students’ Outcome in an Introductory Programming Course: Leveraging the Student BackgroundApplied Sciences10.3390/app13211199413:21(11994)Online publication date: 3-Nov-2023
https://doi.org/10.3390/app132111994
Koutcheme CSarsa SHellas AHaaranen LLeinonen J(2022)Methodological Considerations for Predicting At-risk StudentsProceedings of the 24th Australasian Computing Education Conference10.1145/3511861.3511873(105-113)Online publication date: 14-Feb-2022
https://dl.acm.org/doi/10.1145/3511861.3511873
Kohler JHidalgo LJara J(2022)Using machine learning techniques to predict academic success in an introductory programming course2022 41st International Conference of the Chilean Computer Science Society (SCCC)10.1109/SCCC57464.2022.10000360(1-8)Online publication date: 21-Nov-2022
https://doi.org/10.1109/SCCC57464.2022.10000360
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Predicting success in a first programming course

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Information

Published In

ACM SIGCSE Bulletin Volume 14, Issue 1

Proceedings of the 13th SIGCSE symposium on Computer science education

February 1982

278 pages

ISSN:0097-8418

DOI:10.1145/953051

Issue’s Table of Contents

SIGCSE '82: Proceedings of the thirteenth SIGCSE technical symposium on Computer science education
February 1982
271 pages
ISBN:0897910672
DOI:10.1145/800066
Chairmen:
John F. Dalphin,
Robert A. Barrett,
John T. Gorgone

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 1982

Published in SIGCSE Volume 14, Issue 1

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Cited By

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Köhler JHidalgo LJara J(2023)Predicting Students’ Outcome in an Introductory Programming Course: Leveraging the Student BackgroundApplied Sciences10.3390/app13211199413:21(11994)Online publication date: 3-Nov-2023
https://doi.org/10.3390/app132111994
Koutcheme CSarsa SHellas AHaaranen LLeinonen J(2022)Methodological Considerations for Predicting At-risk StudentsProceedings of the 24th Australasian Computing Education Conference10.1145/3511861.3511873(105-113)Online publication date: 14-Feb-2022
https://dl.acm.org/doi/10.1145/3511861.3511873
Kohler JHidalgo LJara J(2022)Using machine learning techniques to predict academic success in an introductory programming course2022 41st International Conference of the Chilean Computer Science Society (SCCC)10.1109/SCCC57464.2022.10000360(1-8)Online publication date: 21-Nov-2022
https://doi.org/10.1109/SCCC57464.2022.10000360
Sivasakthi MPadmanabhan K(2022)Prediction of Students Programming Performance Using Naïve Bayesian and Decision TreeSoft Computing for Security Applications10.1007/978-981-19-3590-9_8(97-106)Online publication date: 30-Sep-2022
https://doi.org/10.1007/978-981-19-3590-9_8
Prat CMadhyastha TMottarella MKuo C(2020)Relating Natural Language Aptitude to Individual Differences in Learning Programming LanguagesScientific Reports10.1038/s41598-020-60661-810:1Online publication date: 2-Mar-2020
https://doi.org/10.1038/s41598-020-60661-8
Smith DHao QJagodzinski FLiu YGupta VZhang MYang BCooper SLuxton-Reilly A(2019)Quantifying the Effects of Prior Knowledge in Entry-Level Programming CoursesProceedings of the ACM Conference on Global Computing Education10.1145/3300115.3309503(30-36)Online publication date: 9-May-2019
https://dl.acm.org/doi/10.1145/3300115.3309503
Akçay AÇoklar A(2018)Bilişim Teknolojileri ve Yazılım Dersi Öğretmen Adaylarının Programlamaya İlişkin Algılanan Öz Yeterliklerinin Farklı Değişkenler Açısından İncelenmesiInvestigation of Perceived Self-Efficacy of Pre-Service Information Technology and Software Teachers for Programming Regarding Different VariablesKastamonu Eğitim Dergisi10.24106/kefdergi.290426:6(2163-2176)Online publication date: 15-Nov-2018
https://doi.org/10.24106/kefdergi.2904
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
https://dl.acm.org/doi/10.1145/3123814
Morrison BDecker AMargulieux LSheard JTenenberg JChinn DDorn B(2016)Learning LoopsProceedings of the 2016 ACM Conference on International Computing Education Research10.1145/2960310.2960330(221-230)Online publication date: 25-Aug-2016
https://dl.acm.org/doi/10.1145/2960310.2960330
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
https://dl.acm.org/doi/10.1145/2538862.2538962
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