Using Electrodermal Activity Measurements to Understand Student Emotions While Programming

Abstract

Programming can be an emotional experience, particularly for undergraduate students who are new to computer science. While researchers have interviewed novice programmers about their emotional experiences, it can be difficult to pinpoint the specific emotions that occur during a programming session. In this paper, we argue that electrodermal activity (EDA) sensors, which measure the physiological changes that are indicative of an emotional reaction, can provide a valuable new data source to help study student experiences. We conducted a study with 14 undergraduate students in which we collected EDA data while they worked on a programming problem. This data was then used to cue the participants' recollections of their emotions during a retrospective interview about the programming experience. Using this methodology, we identified 21 distinct events that triggered student emotions, such as feeling anxiety due to a lack of perceived progress on the problem. We also identified common patterns in EDA data across multiple participants, such as a drop in their physiological reaction after developing a plan, corresponding with a calmer emotional state. These findings provide new information about how students experience programming that can inform research and practice, and also contribute initial evidence of the value of EDA data in supporting studies of emotions while programming.