Measuring Complexity in Visual Programming for Elementary School Students

Abstract

Visual programming languages are popular because they can be learned at an early age. Unlike text-based languages, they do not require linguistic knowledge. Visual programming languages are diverse and include block programming, flow programming, AR-based programming, and robot-based programming. Scratch and Viscuit are common visual programming tools. Because effective learning methods for these languages have yet to be clarified, we proposed a method and evaluated its learning effectiveness using Chuggington Programming, which is a visual programming game application. Although the questionnaire responses for five workshops at educational institutions were positive, quiz results for one-to two-hour workshops did not show significant differences based on Wilcoxon's signed rank test. Herein we focus on complexity in visual programming to assess the feasibility of a new evaluation method similar to a text-based evaluation method using turtle graphics. We compare traditional text-based evaluation methods such as Cyclomatic Complexity, Halstead Complexity, etc. with turtle graphics. Then we examine the relationship between data for student performance in the workshop and the complexity level. The solution time and complexity are positively correlated, while the percentage of correct responses and complexity are negatively correlated, validating the complexity has potential as an effective learning measure.