Understanding the relationships between self-regulated learning and students source code in a computer programming course

Abstract

To increase the success in computer programming courses, it is important to understand the learning process and common difficulties faced by students. Although several studies have investigated possible relationships between students performance and self-regulated learning characteristics in computer programming courses, little attention has been given to the source code produced by students in this regard. Such source code might contain valuable information about their learning process, specially in a context where practical programming assignments are frequent and students should write source code constantly during the course. This paper presents a strategy to support the correlation analysis among students performance, motivation, use of learning strategies, and source code metrics in computer programming courses. A comprehensive case study is presented to evaluate the proposed strategy through collected data (self-regulated learning characteristics and source code) from 205 undergrad students that accepted to participate voluntarily in the study during three semesters. Results show that the main features from source code which are significantly related to students performance and self-regulated learning features are: length-related metrics, with mainly positive correlations; and Halstead complexity measures, correlated negatively.