The Affective Experience of Novice Computer Programmers
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The Affective Experience of Novice Computer Programmers Nigel Bosch 1 & Sidney D’Mello 1,2
# International Artificial Intelligence in Education Society 2015
Abstract Novice students (N=99) participated in a lab study in which they learned the fundamentals of computer programming in Python using a self-paced computerized learning environment involving a 25-min scaffolded learning phase and a 10-min unscaffolded fadeout phase. Students provided affect judgments at approximately 100 points (every 15 s) over the course of viewing videos of their faces and computer screens recorded during the learning session. The results indicated that engagement, confusion, frustration, boredom, and curiosity were the most frequent affective states, while anxiety, happiness, anger, surprise, disgust, sadness, and fear were rare. Confusion + frustration and curiosity + engagement were identified as two frequently cooccurring pairs of affective states. An analysis of affect dynamics indicated that there were reciprocal transitions between engagement and confusion, confusion and frustration, and one way transitions between frustration and boredom and boredom and engagement. Considering interaction events in tandem with affect revealed that constructing code was the central activity that preceded and followed each affective state. Further, confusion and frustration followed errors and preceded hint usage, while curiosity and engagement followed reading or coding. An analysis of affect-learning relationships after partialling out control variables (e.g., scholastic aptitude, hint usage) indicated that boredom (r=−.149) and frustration (r=−.218) were negative correlated with learning while transitions between confusion → frustration (r=.103), frustration → confusion (r=.105), and boredom → engagement (r=.282) were positively correlated with learning. Implications of the results to theory on affect incidence and dynamics and on the design of affect-aware learning environments are discussed. Keywords Affect . Computer science education . Intelligent tutoring systems
* Nigel Bosch [email protected] 1
Department of Computer Science, University of Notre Dame, Notre Dame, IN 46556, USA
2
Department of Psychology, University of Notre Dame, Notre Dame, IN 46556, USA
Int J Artif Intell Educ
Introduction Computer science (CS) is a difficult degree to complete and has some of the highest attrition rates among undergraduate majors in the U.S. (Haungs et al. 2012). To address this issue, researchers have attempted to identify the factors that contribute to the eventual or failure in computer programming classes. Some of this research has focused on individual differences, such as mathematical ability, programming aptitude, and psychological traits of temperament and motivation (Alspaugh 1972; Blignaut and Naude 2008; Law et al. 2010; Shute and Kyllonen 1990). Many of these factors are somewhat influential in predicting a student’s decision to enroll as well as their eventual success in computer programming courses. However, these trait-based attri
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