The Impact of a Mobile Learning Application on Students’ Cognitive Load and Learning Performance in Biology

Abstract

Aim/Purpose: This study aims to analyze the cognitive load experienced by secondary school students in Biology within m-learning environments and its impact on learning performance. Background: Cognitive load has become a critical issue that schools need to address to ensure students can excel in their learning without being overwhelmed. While principles for reducing cognitive load have been extensively discussed in previous research, studies focusing on mobile learning (m-learning) for Biology among students in Malaysia remain limited. This study employed Cognitive Load Theory (CLT) and Cognitive Theory of Multimedia Learning (CTML) to address this gap. By integrating four key principles—segmenting and pretraining, modality, redundancy, and seductive details—into m-learning tasks using the Successive Approximation Model (SAM1), this study aimed to reduce cognitive load and enhance students’ learning performance. Methodology: This study employed a quantitative approach using a randomized pre-test/post-test quasi-experimental design. Students were randomly assigned to either an intervention group (20 students) or a control group (18 students). The study was conducted over four weeks, comprising a three-week intervention period with a one-week interval. Statistical analyses, including independent t-tests, Mann-Whitney U tests, Quade ANCOVA, and Pearson correlation, were used to analyze the quantitative data. Qualitative feedback was analyzed using thematic analysis. Contribution: This study contributes by providing instructional design strategies that incorporate principles for reducing cognitive load in mobile learning for Biology. It also demonstrates how Cognitive Load Theory (CLT) and Cognitive Theory of Multimedia Learning (CTML) can be effectively integrated. By examining the cognitive load experienced by secondary school students in m-learning environments, the study offers valuable insights for designing and implementing effective instructional strategies. Identifying the factors influencing cognitive load enables educators to develop targeted interventions that enhance learning experiences and optimize performance. Findings: The study indicated that the adoption of mobile learning tasks not only significantly reduced cognitive load but also corresponded to enhanced learning performance. Participants engaging in m-learning experienced lower cognitive load, which was positively associated with superior performance in learning tasks, emphasizing the beneficial impact of mobile learning on cognitive load management and academic achievement. Recommendations for Practitioners: Educators and instructional designers are encouraged to incorporate cognitive load principles into their instructional strategies and learning material design to enhance student performance. Policymakers should consider similar strategies to reduce the cognitive load for students in educational settings to improve learning outcomes. Recommendation for Researchers: Researchers are encouraged to replicate the design elements used in this study when developing mobile or online learning materials to reduce learners’ cognitive load and enhance their performance. They should also consider expanding this research to other topics, subjects, and educational levels to provide further insights and validate the effectiveness of these design elements across different contexts. Impact on Society: The findings of this study have significant implications for society, particularly in addressing mental health and stress issues among the younger generation. By identifying strategies to manage cognitive load and reduce stress in online learning environments, the study provides valuable insights for educators, parents, and policymakers. These strategies can help mitigate the adverse effects of cognitive overload, improve learning experiences, and promote better mental well-being. Additionally, the study’s recommendations can guide the development of more effective and supportive learning environments, contributing to overall societal well-being and academic success. Future Research: Future studies could explore cognitive load beyond the intrinsic and extraneous components focused on in this study, examining additional elements within the frameworks of cognitive load theory and multimedia learning. In addition to using the cognitive load questionnaire, exploring other measurement tools could ensure a more comprehensive understanding of cognitive load. Future research might also consider enriching mobile learning tasks by diversifying subject matter and conducting longitudinal cohort studies. Such studies could provide valuable insights into memory retention over extended periods, aiding in optimizing mobile learning frameworks and enhancing educational experiences.