Behavior-Aware Argumentation: Integrating Toulmin’s Model for Adaptive Learning in Digital Environments

Abstract

This study explores the integration of adaptive learning mechanisms within Viat-map, a learning tool structured around Toulmin’s Argument Model. By analyzing student engagement patterns, particularly in struggling learners and gaming students, the study identifies behavioral trends that impact learning effectiveness. Prior research has demonstrated that Toulmin’s model fosters logical reasoning, yet existing evaluation methods focus primarily on correctness rather than engagement dynamics. To address this gap, Viat-map incorporates behavioral metrics steps taken and time spent as additional indicators of student interaction, enabling a more comprehensive assessment of learning progress. A controlled experiment was conducted with sophomore students, employing pre-test and post-test evaluations alongside ANCOVA and effect size analysis to validate the intervention’s impact. Findings reveal that adaptive learning functionalities significantly enhance student performance, particularly among struggling learners, while mitigating gaming behaviors. These results underscore the importance of behavior-aware analytics in optimizing educational technologies, providing a foundation for future advancements in personalized learning systems