Self-Explanation Effect of Cognitive Load Theory in Teaching Basic Programming

Abstract

Learning basic programming concepts in computer science-related fields poses a challenge for students, to the extent that it becomes an academic-social problem, resulting in high failure and dropout rates. Proposed solutions to the problem can be found in the literature, such as the development of new programming languages and environments, the inclusion of virtual and augmented reality, gamification, automatic grading tools, and intelligent tutoring systems, among others. However, most of these solutions do not explicitly describe the application of some learning theory, instead, they focus on new technologies. Cognitive Load Theory (CLT) is an instructional design theory that aligns the design of instructional materials with human cognitive architecture using 17 design guidelines to optimize learning. The goal of this research is to design, develop, and test instructional materials to support the teaching and learning of basic programming, measuring their effectiveness compared to traditional materials, based on the selfexplanation effect of CLT. To compare the instructional materials, a quasi-experimental design with homogeneous groups was used, involving students from the Autonomous University of Aguascalientes. The results indicate a positive impact of the use of CLT-based instructional materials, either through the application of a single effect or the combination of two effects such as worked example and self-explanation.