Toward Empirical Analysis of Pedagogical Feedback in Computer Programming Learning Environments

Abstract

Digital learning environments are emerging as a key part of the future of computer science education. However, there is little empirical understanding of what forms of didactic feedback are pedagogically optimal for short- and long-term learning outcomes in these new contexts. Methods for classification of feedback in this new context are thus needed, to enable empirical analysis of what constitutes effectiveness. Whilst numerous taxonomies of feedback exist, they do not provide suitable classification for assessing impact of feedback approaches on student learning. We provide an empirically and theoretically meaningful framework for analysing feedback in digital learning environments. The classification is based on placement along two axes – whether feedback is problem or solution centric, and whether it provides information pertaining to a specific instance of a student's work or generalised to the underlying theory. We apply this framework to analyse feedback given in an online computer programming course, showing that types of feedback provided effect attainment of short-term goal-oriented student outcomes. This motivates its possible application in understanding more long-term acquisition and retention of knowledge, both in computer science education and beyond.