The influence of scaffolding for computational thinking on cognitive load and problem-solving skills in collaborative programming

Abstract

This study investigates the effects of metacognitive and cognitive strategies for computational thinking (CT) on managing cognitive load and enhancing problem-solving skills in collaborative programming. Four different scaffolding conditions were provided to help learners optimize cognitive load and improve their problem-solving abilities. A total of 110 participants were randomly assigned to one of the four groups. The four-week experiment included scaffolding, with two hours of Python programming each week to solve two real-world problems. Upon completing the learning process, participants' cognitive load and problem-solving skills were assessed. The results provide empirical evidence that using faded worked examples (WOE) combined with metacognitive scaffolding for CT effectively optimizes cognitive load and enhances problem-solving skills in collaborative programming, leading to improved efficiency and complexity in their solutions.