Classification and Justification of Microlearning Strategies Based on the ADDIE and SAMR Models in Mathematics Education to Promote Self-Directed Learning in Prospective Engineering Students

Abstract

In the contemporary educational context, Microlearning emerges as an effective pedagogical strategy to foster self-directed learning among students, particularly in highly demanding disciplines such as mathematics, which are crucial for engineering programs. However, its implementation requires a structured design and appropriate technological integration. This study proposes a classification of Microlearning tasks and their justification based on the ADDIE and SAMR models, with the aim of optimizing their impact on mathematics learning for prospective engineering students. Through a mixed-methods approach, various types of Microlearning tasks are analyzed in relation to the phases of the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation) and the levels of technological integration in the SAMR model (Substitution, Augmentation, Modification, and Redefinition). The results show that instructional planning based on ADDIE allows for the design of structured tasks tailored to the needs of students in preparatory courses, while the application of the SAMR model supports a progressive transformation of learning through technology use. Identified Microlearning tasks include inter-active exercises, game-based strategies, simulations, adaptive quizzes, videos, and other short audiovisual resources with automated feedback. This research contributes to the field of mathe-matics education by providing a framework for the effective implementation of Microlearning strategies in academic settings, aiming to strengthen self-directed learning and improve perfor-mance among engineering program applicants. Student surveys reveal that Microlearning tasks incorporating gamification elements, videos, and interactive activities have a significant impact on self-learning and mathematical knowledge retention.