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Predicting Programmer Quotient

Meenu Raveendran *, V. G. Renumol

Affiliations

  • Division of IT, School of Engineering, CUSAT, Kochi, Kerala, India

DOI:

Abstract


Computing education necessitates programming skill. But it varies in students. How can we quantify or measure the skill? We are yet to have a standardized measurement system for the programming ability. The concept of Programmer Quotient (PQ), which gives a measure of innate programming ability, attributes a value to one's ability to program, just like Intelligence Quotient (IQ). This would remain the same independent of the programming experience. In this paper, we consider few inherent skills such as Analytical ability, ability to synthesize etc. and try to correlate these skills to one's programming ability. A questionnaire was designed and used to measure the skill in these areas. Then a model was designed from the data collected. It can predict the programming skills of a student from his/her inherent skills, irrespective of the programming language.

Keywords

Programming Ability, Programmer Quotient PQ, Programming Skill, Analytical Ability.

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References


  • Andrew Mcgettrick, Roger Boyle, Roland Ibbet, John Lloyd, Gillian Lovegrove, And Keith Mander;Grand challenges in computing: Education – A summary. Computer Journal Vol. 48 No.1, 2005, 42–48.

  • Sheeson E. Chang; Computer anxiety and perception of task complexity in learning programming-related skills; Computers in Human Behavior; Volume 21, Issue 5 September 2005, Pages 713–728, Elsevier

  • EssiLahtinen, KirstiAla-Mutka, Hannu-MattiJärvinen; A Study of the Difficulties of Novice Programmers; ITiCSE '05 Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education, Pages 14-18; ACM New York, NY, USA ©2005;

  • Raymond Lister, Elizabeth S. Adams, Sue Fitzgerald, William Fone, John Hamer, Morten Lindholm, Robert McCartney, Jan Erik Mostr, Kate Sanders, Otto Sepp¨al¨a, Beth Simon, and Lynda Thomas.; A multi-national study of reading and tracing skills in novice programmers, 2004

  • Michael McCracken, Vicki Almstrum, Danny Diaz, Mark Guzdial, Dianne Hagan, Yifat Ben- David Kolikant, Cary Laxer, Lynda Thomas, Ian Utting, and TadeuszWilusz; A multinational, multi-institutional study of assessment of programming skills of first-year CS students. In workinggroup reports from ITiCSE on Innovation and technology in computer science education, Canterbury, UK, 2001. ACM SIGCSE, 2001 - dl.acm.org.

  • SaeedDehnadi and Richard Bornat; The camel has two humps’; Feb-2006; www.cs.mdx.ac.uk/research/PhDArea/saeed/paper1.pdf

  • D. Mldlan Kurland; Roy D. Pea; Catherine Clement; Ronald Mawby; A Study Of The Development Of Programming Ability And Thinking Skills In High School Students; J. Educational Computing Research, Vol. 2(4), 1986

  • Jeffrey Carver, Lorin Hochstein, Jason Oslin; Programming Ability: Do we know it when we see it? An Empirical Study of Peer Evaluation;

  • JaanaHolvikivi; Conditions for Successful Learning of Programming Skills; N. Reynolds and M. TurcsányiSzabó (Eds.): KCKS 2010, IFIP AICT 324, pp. 155–164, 2010. © IFIP International Federation for Information Processing 2010

  • Richard Bornat, SaeedDehnadi, Simon; Mental models, Consistency and Programming Aptitude; 2008, Australian Computer Society, Inc. Tenth Australasian Computing Education Conference (ACE2008), Wollongong, Australia, January 2008.

  • Simon, Sally Fincher, Anthony Robins; Predictors of Success in a First Programming Course; 2006, Australian Computer Society, Inc. Eighth Australasian Computing Education Conference (ACE2006), Hobart, Tasmania, Australia, January 2006

  • Richard Bornat; Camels and humps: a retraction*; July 24, 2014

  • Jens Bennedsen, Michael E. Caspersen; Abstraction Ability as an Indicator of Success for Learning Computing Science? ICER’08, September 6–7, 2008, Sydney, Australia, ACM

  • Renumol. V. G.,DharanipragadaJanakiram, and Jayaprakash. S. 2010. Identification of cognitive processes of effective and ineffective students during computer programming. ACM Trans. Comput. Educ. 10, 3, Article 10 (August 2010);

  • Ambrosio, A.P, Fábio Moreira Costa, Leandro Almeida, Amanda Franco, and JoaquimMacedo; Identifying cognitive abilities to improve CS1 outcome; 41st ASEE/IEEE Frontiers in Education Conference; October 12 – 15, 2011

  • B. Kaučič*, T. Asič; Improving Introductory Programming with Scratch; MIPRO 2011, May 23-27, 2011, Opatija, Croatia; IEEE

  • SaeedDehnadi; Testing Programming Aptitude; Workshop of the Psychology of Programming Interest Group, 2006 - hssc.sla.mdx.ac.uk

  • MarkkuTukiainen and EeroMönkkönen; Programming aptitude testing as a prediction of learning to program; 14th Workshop of the Psychology of Programming Interest Group, Brunel University, June 2002

  • David CLARK, Testing Programming Skills with Multiple Choice Questions; Informatics in Education, 2004, Vol. 3, No. 2, 161–178

  • Otto Seppala; Advances in assessment of programming skills; Aalto University publication series DOCTORAL DISSERTATIONS, 98/2012

  • Allen and Noel, 2002; Types and levels of educational obje;ctives; 2005 University of Central Florida UCF Academic Program Assessment Handbook; http://oeas.ucf.edu/doc/Bloom_Taxonomy.pdf;

  • http://www.socialresearchmethods.net/kb/dedind.php;

  • http://www.academia.edu/1268673/Translation_Ability_a nd_Translatorial_Competence_Expert_and_Novice_Use_ of_Dictionaries;

  • http://www.businessdictionary.com/definition/decisionmaking.html;

  • http://www.businessdictionary.com/definition/problemsolving.html;

  • National Center for Research on Evaluation, Standards, and Student Testing (CRESST)



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