The Effects of Worked-Out Example and Metacognitive Scaffolding on Problem-Solving Programming

This study explores the effects of worked-out examples and metacognitive scaffolding on novice learners' knowledge performance, cognitive loads, and self-regulation skills in problem-solving programming. 126 undergraduate students in a computer programming fundamentals course were randomly assigned to one of four groups: 1) task performance with a traditional WOE (TW), 2) task performance with a faded WOE (FW), 3) task performance with traditional WOE and metacognitive scaffolding (TWM), and 4) task performance with a faded WOE and metacognitive scaffolding (FWM). Over the course of 3 weeks, participants in these four groups were asked to solve programming problems using Python with WOE and metacognitive scaffolding. The results demonstrate that the provision of metacognitive scaffolding with faded WOE (FWM) is the most effective for problem-solving programming and self-regulation skills. In addition, an interaction effect exists between the two treatments for the germane load in FWM. Therefore, results in this study provide empirical insights into ways to effectively apply WOE and metacognitive scaffolding to problem-solving processes for programming-based complex problem-solving, especially for novice learners.