A genetic algorithm‐based approach for making pairs and assigning exercises in a programming course. Issue 6 (12th October 2020)

• Record Type: Journal Article
• Authors: Tehlan, Kanika; Chakraverty, Shampa; Chakraborty, Pinaki; Khapra, Shradha
• Is Part Of: Computer applications in engineering education. Volume 28:Issue 6(2020)
• Page Start: 1708
• Page End: 1721
• Publication Date: 2020-10-12
• Subjects: collaborative learning -- genetic algorithm -- introductory programming -- pair programming; Engineering -- Study and teaching (Higher) -- Periodicals; Engineering -- Computer-assisted instruction -- Periodicals; 620
• DOI: 10.1002/cae.22349
• Languages: English
• ISSNs: 1061-3773

Abstract

Abstract: Pair programming is an approach where two programmers work to solve one programming problem sitting shoulder to shoulder on a computer. Several studies indicating numerous benefits of using pair programming as a teaching strategy exist. However, only a few of them take into consideration the mechanism followed for pair formation. With an aim to study the impact of pair programming on undergraduate students, we try to make the pairs compatible with a genetic algorithm‐based approach. Using a genetic algorithm, the system ensures that every pair in the class gets a particular combination of skills and personality traits. We also developed a desktop application to assign programming exercises to students dynamically. To assess the efficacy of pair programming in introductory programming course, a formal pair programming experiment was run at Netaji Subhas University of Technology. The pair programming experiment involved a total 171 undergraduate students from a computer engineering course. At the end of the program, we assessed the programming abilities of every student. We also analyzed the impact of a genetic algorithm‐based pairing mechanism. On the basis of assessments, it is observed that pair programming is a successful pedagogical tool for facilitating active learning of introductory programming courses. Responses to survey garnered from undergraduate students hint that the genetic algorithm approach leads to compatible pairs.