Self-Explanation to Support Students Programming Newtonian Trajectories in a Digital Game

• In Event: 41.049 - Stories From the Field: Integrating Computational Thinking Across Curricular Domains

Abstract

Purpose and Theoretical Framework. Computational thinking involves a way of solving problems and designing systems that draws on fundamental concepts of computer science (Wing, 2006, 2008; NRC, 2010, 2011). Computational thinking processes are also central to aspects of scientific practice (Pickering, 1995; Lehrer & Schauble, 2006). Representational competence as well as problem decomposition and abstraction are central elements, but applying these elements in the service of exploring challenging scientific relationships (such as Newtonian mechanics) is a complex endeavor. Prompting students to engage in self-explanation can enhance learning by encouraging students to engage in meta-cognitive activities to monitor what they do and do not understand (Chi, Bassok, Lewis, Reimann, & Glaser 1989; Roy & Chi, 2005; Chi & VanLehn, 1991). The current study presents an approach that adapts the abstraction of self-explanation prompts based on a player’s performance to support players programming and problem-solving Newtonian Trajectories in a digital game.
Methods and Data. The current study was conducted with the 7th grade classrooms of two teachers in two different middle schools in the southeastern United States. A total of 210 students, including 124 from the first school and 86 from the second school, assented to participate in the study. Both schools are racially and culturally diverse. In terms of socio-economics, 43% of the students at the first school and 83% of the students at the second school are eligible for free or reduced lunch. Students were randomly assigned to one of three game conditions within each classroom (i.e., each classroom included students in all three conditions). In the navigation-only condition, players programmed their trajectories without any self-explanation prompts. In the navigation+abstract condition, these navigational challenges are paired with a self-explanation prompt that focuses on abstract connections between the navigational challenges and Newtonian relationships. In the navigation+adaptive condition, the navigational challenges are paired with self-explanation prompts that adaptively increase from low abstraction (in which the prompts focus concretely on navigational moves) to high abstraction (in which the prompts focus more abstractly on the navigational challenges in terms of overarching Newtonian relationships).
Results. The results demonstrate that students in the navigation+adaptive condition (a) scored significantly higher on the post-test than students whose self-explanation prompts were not adaptively adjusted and were always abstract and (b) scored higher, but not significantly so, than students who did not receive self-explanation functionality. Analyses of game-play metrics suggest that trade-offs in terms of progress through the game may explain some aspects of these post-test comparisons. Analyses also demonstrate that both self-explanation conditions significantly outperformed the navigation-only comparison condition on a game-play metric that suggests deeper model-based thinking.
Significance. The current study presents an approach that adapts the abstraction of self-explanation prompts based on a player’s performance can support players’ computational thinking while programming Newtonian trajectories in a digital game. The study clarifies the value of adaptive self-explanation scaffolding in supporting both learning as well as deeper understanding and computational thinking.

Authors

• Doug Clark, Vanderbilt University

• Satyugjit Singh Virk, Teachers College, Columbia University

• Jackie Barnes, Northeastern University

• Deanne Marie Adams, University of Notre Dame