Sustainable Mobile Microlearning: Evaluating Learners' Perceptions and Learning Outcomes in IT Education

Abstract

Mobile Microlearning (MML) has emerged as a sustainable digital learning strategy capable of improving cognitive efficiency, reducing learner fatigue, and supporting scalable instructional delivery. This study investigates how MML compares with conventional Mobile Learning (ML) when both formats deliver identical instructional content in an introductory programming module for undergraduate IT students. Sixty-eight students were randomly assigned to either an ML course or a redesigned MML course built from the same curriculum but reorganized into short, interactive micro-units. Learners completed a pre/post multiple-choice programming test and a five-scale Course Characteristics Questionnaire evaluating Ease of Use, Clarity & Coherence, Appeal, Difficulty, and Focus. Results indicated significant learning gains in both groups; however, the MML group demonstrated substantially greater improvement. Appeal, Difficulty, and Focus were the strongest predictors of learning growth, with Appeal and Difficulty significantly mediating the relationship between course format and performance. Because the two formats differed only in delivery design (not in content), the findings highlight micro-temporal structuring, lightweight interaction, and immediate feedback as key mechanisms driving the superiority of MML. Grounded in sustainability principles, the study shows that MML reduces cognitive load, enables more efficient study patterns, and provides a scalable, reusable content structure that supports accessible, resilient learning ecosystems. These results offer design-level insights for creating sustainable mobile instructional experiences in higher education IT programs.