Premium
A genetic algorithm‐based approach for making pairs and assigning exercises in a programming course
Author(s) -
Tehlan Kanika,
Chakraverty Shampa,
Chakraborty Pinaki,
Khapra Shradha
Publication year - 2020
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.22349
Subject(s) - pair programming , genetic programming , computer science , class (philosophy) , computer programming , inductive programming , genetic algorithm , programming paradigm , artificial intelligence , programming language , machine learning , software , software development
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.