Two Approaches for Programming Education in the Domain of Graphics: An Experiment

TL;DR

This study compares two graphics programming approaches in Python, finding similar learning outcomes but higher accuracy in tracing compositional graphics, and confirms graphics as an engaging domain for teaching introductory programming.

Contribution

It provides empirical evidence on the effectiveness of compositional graphics versus turtle graphics in programming education, highlighting engagement and transfer of learning.

Findings

Few differences in post-test scores between groups

Higher accuracy in tracing compositional graphics programs

Both groups reported high engagement and perceived learning

Abstract

Context: Graphics is a popular domain for teaching introductory programming in a motivating way, even in text-based programming languages. Over the last few decades, a large number of libraries using different approaches have been developed for this purpose. Inquiry: Prior work in introductory programming that uses graphics as input and output has shown positive results in terms of engagement, but research is scarce on whether learners are able to use programming concepts learned through graphics for programming in other domains, transferring what they have learned. Approach: We conducted a randomized, controlled experiment with 145 students as participants divided into two groups. Both groups programmed using graphics in Python, but used different approaches: one group used a compositional graphics library named PyTamaro; the other used the Turtle graphics library from Python's standard library. Student engagement was assessed with surveys, and programming knowledge with a post-test on general programming concepts and programming tasks in the domain of graphics. Knowledge: We find few differences between the two groups on the post-test, despite the PyTamaro group having practiced on problems isomorphic to those in the post-test. The participants traced a compositional graphics program more accurately than a 'comparable' turtle graphics program. Both groups report high engagement and perceived learning; both perform well on simple program-writing tasks to create graphics. Grounding: Our findings are based on a controlled experiment with a count of 145 participants, which exceeds the sample size indicated by power analysis to detect a medium effect size. The complete instrument and teaching materials used in the study are available as appendixes. Importance: This study adds further evidence that graphics is an engaging domain for introductory programming; moreover, it shows that the compositional graphics approach adopted by PyTamaro yields engagement levels comparable to the venerable turtle approach. Compositional graphics code appears to be easier to trace than turtle graphics code. As for conceptual knowledge, our results indicate that practicing on programming tasks isomorphic to those of the test can still not be enough to achieve better transfer. This challenges programming educators and researchers to investigate further which graphics-based approaches work best and how to facilitate transfer.