Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis

Abstract

Pair programming is a model of collaborative learning. It has become a well-known pedagogical practice in teaching introductory programming courses because of its potential benefits to students. This study aims to investigate pair patterns in the context of pair program tracing and debugging to determine what characterizes collaboration and how these patterns relate to success, where success is measured in terms of performance task scores. This research used eye-tracking methodologies and techniques such as cross-recurrence quantification analysis. The potential indicators for pair success were used to create a model for predicting pair success. Findings suggest that it is possible to create a model capable of predicting pair success in the context of pair programming. The predictors for the pair success model that can obtain the best performance are the pairs’ proficiency level and degree of acquaintanceship. This was achieved using an ensemble algorithm such as Gradient Boosted Trees. The performance of the pairs is largely determined by the proficiency level of the individuals in the pairs; hence, it is recommended that the struggling students be paired with someone who is considered proficient in programming and with whom the struggling student is comfortable working with.