Computer Programming Aptitude Test as a Tool for Reducing Student Attrition

Authors

DOI:

https://doi.org/10.17770/etr2015vol3.175

Keywords:

aptitude test, attrition rate, computer science education, data analysis

Abstract

The stable trend to lose from one-third to half of students in the first study year of computing studies motivated us to explore, which methods are used to determine in advance such applicants, who have no change to overcome the first study year. Initially, a research about the factors influencing the attrition in Faculty of Computing at the University of Latvia was conducted. The research revealed that the trend of non-beginning studies might indicate the wrong choice of the study field and possible lack of understanding of what is programming by enrolled students (applicants as well as pupils).

The study provides the review of the situation with programming aptitude tests in the world, which could serve as one of the solutions to the dropout reduction. An action plan is proposed, which is based on the exploration of students and evaluation of activities already conducted at the Faculty of Computing of the University of Latvia to reduce dropout (School of Young Programmers, Compensative Course in High School Mathematics, Mentoring programs). Moreover, the supplementation of these activities by one of the existing programming attitude tests (or a combination of several tests) or a necessity to develop a new similar test is considered.

Downloads

Download data is not yet available.

References

J. Borzovs, L. Niedrīte, and D. Solodovņikova, “Factors affecting attrition among first year computer science students: the case of University of Latvia”, 2015, submitted for publication.

Computing at School, [Online]. Available: http://www.computingatschool.org.uk/ [Accessed: Mar.17., 2015]

D.Bricklin, “Why Johnny can't program”, August 2002, [Online], Available: http://www.bricklin.com/wontprogram.htm [Accessed: Mar.17., 2015]

F. Araque, C. Roldán, and A. Salguero, “Factors influencing university drop out rates”, Computers and Education, vol. 53(3), pp. 563-574, 2009.

L. Paura and I. Arhipova, “Cause Analysis of Students’ Dropout Rate in Higher Education Study Program”, Procedia-Social and Behavioral Sciences, vol. 109, pp. 1282-1286, 2014.

L. Grebennikov and M. Shah, "Investigating attrition trends in order to improve student retention", Quality Assurance in Education, Vol. 20 (3), pp. 223 – 236, 2012.

“School of Young Programmers” [Online]. Available: http://www.df.lu.lv/nacstudet/jauno-datoriku-skola/, [Accessed: Mar.17., 2015].

T.C. Rowan, “Psychological tests and selection of computer programmers”, Journal of the Association for Computing Machinery, Vol. 4, pp. 348-353, 1957.

W.J. McNamara, J.L. Hughes, “Manual for the revised Programmer Aptitude Test”, New York: international Business Machines Corporation, 1959.

A.W. Stalnaker, “The Watson-Glaser Critical Thinking Appraisal as a predictor of programming performance”, Proceedings of the Third Annual Computer Personnel Research Group, pp. 75-77, 1965.

E. Spranger, “ Types of Men: The Psychology and Ethics of Personality”, Johnson Reprints, New York, 1966.

C.K. Capstick, J.D. Gordon, and A. Salvadori, “Predicting performance by university students in introductory computing courses”, ACM SIGCSE Bulletin Vol. 7(3), pp. 21-29, 1975.

R.E. Mayer, J.L. Dyck, and W. Vilberg, “Learning to program and learning to think: What’s the connection?” Commun. of ACM Vol.29 (7), pp. 605-610, 1986.

D.A. Scanlan “The mental abilities associated with programming aptitude”, CSC'88 Proceedings of the ACM sixteenth annual conference on computer science, p.737, 1988.

G.E. Evans and M.G. Simkin, “What best predicts computer proficiency?” Commun. of ACM Vol.32(11), pp. 1322–1327, 1989.

C. Bishop-Clark and D.D. Wheeler, “The Myers-Briggs personality type and its relationship to computer programming”, Journal of Research on Computing in Education, Vol. 26(3), pp. 358-370, 1994.

C.C. Cegielski, J. Dianne, and D.J. Hall, “What makes a good programmer?”, Commun. of the ACM, Vol. 49(10), pp. 73-75, 2006.

S. Wray, “SQ Minus EQ can Predict Programming Aptitude”, Proceedings of the PPIG 19th Annual Workshop, pp. 243-254, 2007.

M. Tukiainen and E. Mönkkönen, “Programming aptitude testing as a prediction of learning to program”, In: J. Kuljis, L. Baldwin, and R. Scoble (Eds). Proc. PPIG 14, pp. 45-57, 2002

S. Dehnadi, R. Bornat “The camel has two humps”, 2006, [Online], Available: http://wiki.t-o-f.info/uploads/EDM4600/The%20camel%20has%20two%20humps.pdf [Accessed: Mar.17., 2015]

T. Lorenzen, H.-L. Chang, “MasterMind©: a predictor of computer programming aptitude”, ACM SIGCSE Bulletin, Vol. 38 (2), pp. 69-71, 2006.

University of Kent, Computer Programming Aptitude Test [Online], Available: http://www.kent.ac.uk/careers/tests/computer-test.htm [Accessed: Mar.17., 2015]

Carl Jung’s and Isabel Briggs Myers’ typology test, [Online], Available: http://www.humanmetrics.com/hr/you/personalitytype.aspx [Accessed: Mar.17., 2015]

Autism Research Centre, Cambridge, “Cambridge Personality Questionnaire” (SQ test). Available: http://www.autismresearchcentre.com/arc_tests, [Accessed: Mar.17., 2015]

Autism Research Centre, Cambridge, “The Cambridge Behaviour Scale” (EQ test), Available: http://www.autismresearchcentre.com/tests/eq_test.asp, [Accessed: Mar.17., 2015]

Android Apps on Google Play: Aptitude and Logical Reasoning , [Online]. Available: https://play.google.com/store/apps/details?id=com.madguy.aptitude.lr [Accessed: Mar.17., 2015]

I. Gorbāns, “Programming environment for every students – Scratch or basics of programming in few hours”, E-book, 2014, [Online]. Available: http://skolas.lu.lv/mod/book/view.php?id=29857 , [Accessed: Mar.17., 2015]

Mentoring program of Faculty of Computing at University of Latvia, [Online]. Available: http://www.df.lu.lv/zinas/t/28406/ [Accessed: Mar.17., 2015]

Downloads

Published

2015-06-16

How to Cite

[1]
J. Borzovs, L. Niedrite, and D. Solodovnikova, “Computer Programming Aptitude Test as a Tool for Reducing Student Attrition”, ETR, vol. 3, pp. 29–35, Jun. 2015, doi: 10.17770/etr2015vol3.175.