Research Article Open Access

INVESTIGATION OF STUDENT'S PERSONALITY ON PAIR PROGRAMMING TO ENHANCE THE LEARNING ACTIVITY IN THE ACADEMIA

V. Venkatesan1 and A. Sankar1
  • 1 , India
Journal of Computer Science
Volume 10 No. 10, 2014, 2020-2028

DOI: https://doi.org/10.3844/jcssp.2014.2020.2028

Submitted On: 11 January 2014 Published On: 26 June 2014

How to Cite: Venkatesan, V. & Sankar, A. (2014). INVESTIGATION OF STUDENT'S PERSONALITY ON PAIR PROGRAMMING TO ENHANCE THE LEARNING ACTIVITY IN THE ACADEMIA. Journal of Computer Science, 10(10), 2020-2028. https://doi.org/10.3844/jcssp.2014.2020.2028

Abstract

Agile Software development is now a global phenomenon and is rapidly becoming organizations' most preferred IT process. Extreme Programming (XP) is one of the Agile Methods and Collaborative Pair Programming (CPP) is one of the very important practices of eXtreme Programming. Agile focuses team work which is very important in the field of software development. The software industry has practiced CPP, where two programmers working side by side on one computer on the same problem with great success. Similar experiments have been conducted in academia and pair programming has been shown to be beneficial for both students and teaching staff in university courses. In this study, we conducted some set of experiments about the "human" aspect of the CPP; in particular the effects that personality attributes may have on pair programmer's effectiveness as a pedagogical tool. A formal experiment has been conducted during 2012-13 odd semester at the PSG College of Technology, India to investigate the influence of personality differences among paired students using the five-factor model as a personality measurement framework. The aim of this research is to improve the implementation of CPP as a pedagogical tool to the academic setup through understanding the impact of the variation in the personality profile of paired students towards their academic performance.

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Keywords

  • Pair Programming
  • Extreme Programming Five Factor Model