The Impact of Guided Metacognitive Feedback on Novice Programmers Using Learning by Teaching Environment

Abstract

Learning-by-teaching is a powerful approach that enhances students to think deeply, orally and repeatedly. Several computer-based systems have been implemented where students play the teacher role and virtual agents play the tutee role. The existing systems focus on various domains, but none of them has considered programming problem solving. Additionally, the majority of these systems did not provide metacognitive support. They only focus on providing feedback as correct answers, and this type of feedback is called knowledge of correct response. However, this paper explores the influence of guided metacognitive feedback on novice programmers in a teachable agent environment. For that, a computer-based learning environment is built to enable the novice programmers to teach programming problem solving to an animated agent. It combines learning-by-teaching technique and metacognitive support in order to assist those beginners to acquire comprehensive learning on how to solve unfamiliar problems and prepare those programmers for future learning tasks. We conduct an experiment to compare the effect of the aforementioned feedbacks on the novice programmers’ performance in learning-byteaching paradigm. The results show that the metacognitive feedback has positive effect on novice programmers’ achievement of solving problems. In addition, providing metacognitive feedback as explicit feedback in learning-by-teaching paradigm improves the novices' abilities to estimate what they know and what they do not know about how to solve new programming problems.