Analyzing the Synergistic Effects of Cognitive Load Reduction and Schema-Based Instruction on Enhancing the Learning of Mathematical Formulas among Twelfth-Grade Humanities Students

Document Type : Original Article

Authors

1 Assistant Professor, Department of Mathematics Education, Arvand International Branch, Islamic Azad University, Abadan, Iran

2 Master’s Degree in Mathematics Education, Ministry of Education, Khuzestan Province, Abadan, Iran

3 Assistant Professor, Department of Mathematics Education, Arvand International Branch, Islamic Azad University, Abadan, Iran.

Abstract

The purpose of this study was to compare the traditional problem-solving approach and exercises presented in the Mathematics and Statistics 3 textbook with a stepwise decomposition approach, in which complex formulas were divided into multiple stages to develop mental schemata and deepen students’ conceptual understanding. The study also examined the effect of this method on students’ performance in the final examination. This research is an applied study employing a quasi-experimental design with an experimental and a control group. The first-term scores of the students were used as a pretest, and their second-term final scores served as the posttest. The statistical population included all 12th-grade humanities students in Abadan during the 2023–2024 academic year, and the accessible sample consisted of 56 students randomly assigned to two groups of 28 students each. In the experimental group, instruction during the second term was conducted using a schema-based approach through the decomposition and simplification of problem-solving steps to create an algorithm for better conceptual understanding. The control group received instruction following the conventional textbook method. Data were analyzed using analysis of covariance (ANCOVA). The results indicated that although student performance generally tends to decline during the second term, the experimental group outperformed the control group significantly. The schema-based instructional method effectively improved this trend (p = 0.024), with an effect size of 0.092, indicating a medium effect.