Article

Case studies in admissions to and early performance in computer science degrees

Authors:

Sylvia Alexander,

Dermot Shinners-KennedyAuthors Info & Claims

ITiCSE-WGR '03: Working group reports from ITiCSE on Innovation and technology in computer science education

Pages 137 - 147

https://doi.org/10.1145/960875.960541

Published: 30 June 2003 Publication History

Abstract

We present seven case-studies of undergraduate recruitment to Computer Science courses together with analysis of students' success during the early part of their study. We focus particularly upon qualification on entry, the subjects studied in the early university curriculum, and student grades.We find that while university admissions are complex processes, there exists sufficient commonality to permit some useful comparisons. These suggest that predicting undergraduate performance on the basis of entry qualifications is fraught. Nevertheless, it seems that students who arrive at university with a record of success in earlier studies may be more likely to succeed than otherwise. In particular, good grades in pre-university study may indicate that they are more likely to do well in the mathematical part of the university curriculum. Conversely, we find nothing in entry qualifications to indicate which students will be successful in the study of programming.

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Cited By

Fong SSi YBiuk-Aghai R(2009)Applying a hybrid model of neural network and decision tree classifier for predicting university admissionProceedings of the 7th international conference on Information, communications and signal processing10.5555/1818318.1818320(1-5)Online publication date: 8-Dec-2009
https://dl.acm.org/doi/10.5555/1818318.1818320
Fong SSi YBiuk-Aghai R(2009)Applying a hybrid model of neural network and decision tree classifier for predicting university admission2009 7th International Conference on Information, Communications and Signal Processing (ICICS)10.1109/ICICS.2009.5397665(1-5)Online publication date: Dec-2009
https://doi.org/10.1109/ICICS.2009.5397665
Hayes JDekhtyar AHolbrook ASundaram SDekhtyar O(2006)Will Johnny/Joanie Make a Good Software Engineer? Are Course Grades Showing the Whole Picture?Proceedings of the 19th Conference on Software Engineering Education & Training10.1109/CSEET.2006.42(175-184)Online publication date: 19-Apr-2006
https://dl.acm.org/doi/10.1109/CSEET.2006.42
Show More Cited By

Index Terms

Case studies in admissions to and early performance in computer science degrees
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

cover image ACM Conferences

ITiCSE-WGR '03: Working group reports from ITiCSE on Innovation and technology in computer science education

June 2003

291 pages

ISBN:9781450374538

DOI:10.1145/960875

Conference Chairs:
Vassilios Dagdilelis
University of Macedonia, Greece
,
Maya Satratzemi
University of Macedonia, Greece
,
Editor:
David Finkel
Worcester Polytechnic Institute
,
Program Chairs:
Roger Boyle
University of Leeds, UK
,
Georgios Evangelidis
University of Macedonia, Greece

ACM SIGCSE Bulletin Volume 35, Issue 4
December 2003
144 pages
ISSN:0097-8418
DOI:10.1145/960492
Issue’s Table of Contents

Copyright © 2003 ACM.

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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SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

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Published: 30 June 2003

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ITiCSE03: 8th Annual Conference on Innovation and Technology in Computer Science Education

June 30 - July 2, 2003

Thessaloniki, Greece

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Overall Acceptance Rate 552 of 1,613 submissions, 34%

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Cited By

Fong SSi YBiuk-Aghai R(2009)Applying a hybrid model of neural network and decision tree classifier for predicting university admissionProceedings of the 7th international conference on Information, communications and signal processing10.5555/1818318.1818320(1-5)Online publication date: 8-Dec-2009
https://dl.acm.org/doi/10.5555/1818318.1818320
Fong SSi YBiuk-Aghai R(2009)Applying a hybrid model of neural network and decision tree classifier for predicting university admission2009 7th International Conference on Information, Communications and Signal Processing (ICICS)10.1109/ICICS.2009.5397665(1-5)Online publication date: Dec-2009
https://doi.org/10.1109/ICICS.2009.5397665
Hayes JDekhtyar AHolbrook ASundaram SDekhtyar O(2006)Will Johnny/Joanie Make a Good Software Engineer? Are Course Grades Showing the Whole Picture?Proceedings of the 19th Conference on Software Engineering Education & Training10.1109/CSEET.2006.42(175-184)Online publication date: 19-Apr-2006
https://dl.acm.org/doi/10.1109/CSEET.2006.42
Shea CShea SKilleavy M(2006)The post-primary computing experiences of institute of technology computing studentsIrish Educational Studies10.1080/0332331060059768325:1(107-119)Online publication date: Mar-2006
https://doi.org/10.1080/03323310600597683
Baghban Karimi OToti GGutica MRobinson RZhang LPaterson JLindner PO'Dea MLaakso MMonga MSimon Sheard J(2023)Enhancing Diversity and Inclusion in Computer Science Undergraduate Programs: The Role of AdmissionsProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3623762.3633496(1-29)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3623762.3633496
Virkki O(2022)Reasoning Skills Assessment in Information Technology National Entrance Examination Reform; a Design Science Approach2022 IEEE Global Engineering Education Conference (EDUCON)10.1109/EDUCON52537.2022.9766817(719-726)Online publication date: 28-Mar-2022
https://doi.org/10.1109/EDUCON52537.2022.9766817
Virkki OSherriff MMerkle LCutter PMonge ASheard J(2021)Computer Science Student Selection A Scoping Review and a National Entrance Examination ReformProceedings of the 52nd ACM Technical Symposium on Computer Science Education10.1145/3408877.3432371(654-659)Online publication date: 3-Mar-2021
https://dl.acm.org/doi/10.1145/3408877.3432371
Adamopoulos F(2020)Learning Programming, Success FactorsEncyclopedia of Education and Information Technologies10.1007/978-3-030-10576-1_181(1067-1072)Online publication date: 14-Jun-2020
https://doi.org/10.1007/978-3-030-10576-1_181
Adamopoulos F(2019)Learning Programming: Success FactorsEncyclopedia of Education and Information Technologies10.1007/978-3-319-60013-0_181-1(1-6)Online publication date: 14-Nov-2019
https://doi.org/10.1007/978-3-319-60013-0_181-1
Falkner NFalkner KClear ASanders KSimon B(2012)A fast measure for identifying at-risk students in computer scienceProceedings of the ninth annual international conference on International computing education research10.1145/2361276.2361288(55-62)Online publication date: 9-Sep-2012
https://dl.acm.org/doi/10.1145/2361276.2361288
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