Understanding Student Computational Thinking with Computational Modeling

TL;DR

This study investigates 9th grade students' understanding of computational thinking in physics through modeling tasks, revealing links between physics comprehension, causal reasoning, and programming success.

Contribution

It introduces an assessment framework for student computational thinking in physics and highlights the importance of causal understanding for programming success.

Findings

One-third of students successfully completed the programming task.

Success correlated with understanding physics and computation integration.

Causal reasoning about force and motion improved programming outcomes.

Abstract

Recently, the National Research Council's framework for next generation science standards highlighted "computational thinking" as one of its "fundamental practices". 9th Grade students taking a physics course that employed the Modeling Instruction curriculum were taught to construct computational models of physical systems. Student computational thinking was assessed using a proctored programming assignment, written essay, and a series of think-aloud interviews, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Roughly a third of the students in the study were successful in completing the programming assignment. Student success on this assessment was tied to how students synthesized their knowledge of physics and computation. On the essay and interview assessments, students displayed unique views of the relationship between force and motion; those who spoke of this relationship in causal (rather than observational) terms tended to have more success in the programming exercise.