Applying Cognitive Load Theory and the Split Attention Effect to Learning Data Structures

Abstract

Learning data structures is a hard task for computer science students, given the mental effort required to simultaneously understand abstract diagrams and the dynamic manipulation of nodes and pointers using programming languages. In literature, proposed solutions to the problem focus on visualization-based artifacts, pedagogical methods, or a combination of both. The present study is framed within the cognitive learning paradigm and describes the design and testing of a linked list visualization software tool, based on the Split Attention effect of Cognitive Load Theory. The study was carried out at the Autonomous University of Aguascalientes (UAA), Mexico. In the learning effectiveness test, significant results (p=0.000) are reported for the participants of the experimental group (n=35), using the nonparametric Wilcoxon test, with a quasi-experimental pre-post test design. It is discussed that the spatial and temporal integration of linked list node diagrams and the corresponding worked example code for the implementation of its basic operations can benefit students with learning gaps in previous introductory programming courses. It is also reported that the control group (n=36) had gains through traditional learning (p=0.022), although this group started from a higher prior academic performance. We propose to extend the Split Attention Tool to include a wider range of data structures and to replicate the study with randomized experimental designs.