Fostering Computational Thinking through Whole-Task Learning: A 4C/ID-Based Instructional Framework

Abstract

In the rapidly evolving digital era, cultivating innovative talents with the ability to solve complex real-world problems has become a strategic priority for global education reform. As a ubiquitous problem-solving methodology, Computational Thinking (CT) serves as a vital cognitive bridge between digital technologies and human reasoning. Computational Thinking is globally recognized as a critical competency for the 21st century and a cornerstone of STEM education. However, current K-12 programming instruction often suffers from a "syntax-driven" approach and knowledge fragmentation, where novice learners experience cognitive overload when facing authentic problems. This creates a barrier to the transfer of algorithmic skills. To address these challenges, this study proposes a holistic instructional design framework based on the Four-Component Instructional Design (4C/ID) model. We constructed a theoretical mapping between the cognitive mechanisms of CT and the four components of the model. Specifically, Learning Tasks provide the authentic context for problem decomposition; Supportive Information scaffolds non-recurrent cognitive strategies, including abstraction, generalization, and iteration; while Procedural Information and Part-task Practice facilitate the automation of recurrent skills such as algorithms and debugging. By integrating these components, the framework effectively manages cognitive load and shifts the pedagogical focus from isolated coding drills to deep, transferable problem-solving capabilities. This study provides a systematic blueprint and theoretical basis for educators to design effective, whole-task CT instruction.

Keywords: Computational Thinking; 4C/ID Model; Instructional Design; Cognitive Load Theory; Whole-task Learning

DOI: 10.7176/JEP/17-1-09

Publication date: January 30th 2026