The Affective Experience of Novice Computer Programmers
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- 作者：Nigel Bosch…
- 关键词：Affect ; Computer science education ; Intelligent tutoring systems
- 刊名：International Journal of Artificial Intelligence in Education
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：27
- 期：1
- 页码：181-206
- 全文大小：
- 刊物类别：Artificial Intelligence (incl. Robotics); Educational Technology; User Interfaces and Human Computer
- 刊物主题：Artificial Intelligence (incl. Robotics); Educational Technology; User Interfaces and Human Computer Interaction; Computers and Education;
- 出版者：Springer New York
- ISSN：1560-4306
- 卷排序：27
文摘
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 co-occurring 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.