Do You Think You Can? The Influence of Student Self-Efficacy on the Effectiveness of Tutorial Dialogue for Computer Scie
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Do You Think You Can? The Influence of Student Self-Efficacy on the Effectiveness of Tutorial Dialogue for Computer Science Joseph B. Wiggins 1 & Joseph F. Grafsgaard 1 & Kristy Elizabeth Boyer 2 & Eric N. Wiebe 1 & James C. Lester 1
# International Artificial Intelligence in Education Society 2016
Abstract In recent years, significant advances have been made in intelligent tutoring systems, and these advances hold great promise for adaptively supporting computer science (CS) learning. In particular, tutorial dialogue systems that engage students in natural language dialogue can create rich, adaptive interactions. A promising approach to increasing the effectiveness of these systems is to adapt not only to problem-solving performance, but also to a student’s characteristics. Self-efficacy refers to a student’s view of her ability to complete learning objectives and to achieve goals; this characteristic may be particularly influential during tutorial dialogue for computer science education. This article examines a corpus of effective human tutoring for computer science to discover the extent to which considering self-efficacy as measured within a pre-survey, coupled with dialogue and task events during tutoring, improves models that predict the student’s self-reported frustration and learning gains after tutoring. The analysis reveals that students with high and low self-efficacy benefit differently from tutorial dialogue. Student control, social dialogue, and tutor moves to increase efficiency, may be particularly helpful for high self-efficacy students, while for low selfefficacy students, guided experimentation may foster greater learning while at the same time potentially increasing frustration. It is hoped that this line of research will enable tutoring systems for computer science to tailor their tutorial interactions more effectively. Keywords Self-efficacy . Tutorial dialogue . Computer science education
* Joseph B. Wiggins [email protected]
1
North Carolina State University, Raleigh, NC, USA
2
University of Florida, Gainesville, FL, USA
Int J Artif Intell Educ
Introduction One-on-one tutoring has been shown to be more effective than conventional classroom settings (Bloom 1984). Due to the disparity between individualized tutoring and conventional classrooms, human tutoring has been the subject of considerable investigation (Chi et al. 2001; McKendree 1990; VanLehn 2011). Studies have examined the interaction between students and tutors from multiple standpoints: cognitive and affective outcomes (Boyer et al. 2008), the adaptive presentation of instructional material (D’Mello et al. 2009), motivational strategies (Lepper et al. 1993), and the exchange of rich natural language dialogue (Litman et al. 2009). Development of tutorial dialogue systems presents many challenges, but these systems can currently engage with students in topic areas such as physics (Chi. M et al. 2010), electricity and electronics (Dzikovska et al. 2010), logic (Croy et al. 2007), and, as is the focus of this paper, computer
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