A comparative analysis of coding approaches in machine learning among computer science students and non-computer science students

Abstract

The increasing presence of Machine Learning in all fields of study requires an improvement in how it is taught. Previous research on this topic examined how to teach ML concepts and highlighted the importance of using technology and leveraging relevant pedagogical content knowledge. It did not compare the impact of previous programming knowledge on the students' approach to solving ML problems. This paper explores the differences in implementation of 60 Machine Learning coding assignments using metrics that were determined by previous research to be a good indicator of code quality and computational thinking. By analysing the code submissions with these metrics, the results show several interesting insights about the students' use of functions, variables and the explanations of their thought process. However, results of the metrics are mostly inconclusive. The results from this study highlight the need for additional research on this topic to ensure that people with limited Computer Science knowledge are able to learn about it and implement it in their disciplines.