Enhancing Algorithmic Thinking Through Graph-Theoretic Unplugged Activities

Abstract

There is a growing interest in promoting algorithmic thinking, making it a significant and emerging area of study. Despite this, limited research explores how unplugged learning activities, particularly in graph theory, influence students' algorithmic thinking (AT). This study aims to address this gap by developing unplugged learning activities based on the minimum spanning tree (MST) concept. The unplugged learning activities include a series of MST tasks to scaffold students' problem-solving. Specifically, this study seeks to investigate which MST algorithms students may discover. The findings indicate that the unplugged learning activities help students independently solve MST tasks. They discovered two types of algorithms for solving MST problems: Kruskal's algorithm and a combination of Kruskal's and Prim's algorithms. However, they struggled to write the algorithm in a way that anyone could follow and achieve the same result. Therefore, we propose revising the activities by adding blockbased command tasks to help them develop their AT.