Problem Decomposition in Introductory Computer Science and Spatial Reasoning

Abstract

Previous research has documented correlations between spatial reasoning ability and success in STEM fields [7-9]. While connection between spatial reasoning and other STEM fields like physics and calculus may seem obvious, there are no theories to explain this correlation of CS (computer science) performance and spatial reasoning. [5,7,8]. We aim to better understand this correlation, specifically between CS performance in an introductory course and spatial reasoning by observing the common characteristics between students' strategies in solving CS and spatial reasoning problems. We conducted interviews with eight students who have prior experience in CS but have taken only two introductory CS courses. In the interview, we asked the participants to solve a total of four problems; two CS problems and two spatial reasoning problems. [1,6]. The CS problems were code-reading problems in Python, which focus on basic programming concepts, including nested loops and functions. We analyzed the participants' problem-solving strategies and recorded their explanation for each problem. We observed for both types of problem, participants appeared to first observe the problem for its fundamental structures then decomposes the problem into smaller sub-problems. We conjecture that problem decomposition may be a required skill to solve both CS and spatial reasoning problems, and thus, could be a possible factor that contributes to the correlation between the two fields. Further study into utilization of problem decomposition in CS and spatial reasoning may provide more insights into the correlation between success in CS and spatial reasoning ability.