Abstract: In recent years, the field of education has witnessed a growing interest in personalized learning and the potential of microlearning as a transformative pedagogical approach. While these concepts hold promise for enhancing student engagement and achievement, there remains a need to systematically investigate the variables that are uncontested, as well as the transgressions that may arise when implementing personalized microlearning in educational settings. This article presents a comprehensive analysis of the current literature pertaining to personalized microlearning, aiming to identify the crucial variables and potential transgressions that may impact its effectiveness.
This study adopts a systematic review methodology, analyzing a corpus of research studies, theoretical frameworks, and empirical evidence. The analysis focuses on identifying key variables that have been consistently supported across multiple studies, such as learner autonomy, individualized pacing, and adaptive feedback. Additionally, the study explores the transgressions that may occur during the implementation of personalized microlearning, including issues related to privacy, equity, and the potential marginalization of certain student groups.
The findings of this study shed light on the critical factors that significantly influence the success of personalized microlearning initiatives. By identifying uncontested variables, educators and policymakers can develop evidence-based strategies for optimizing student learning experiences. Furthermore, the exploration of transgressions provides a framework for anticipating and mitigating potential risks associated with personalized microlearning implementation.
- Exploitation of micro-learning for generating personalized learning paths
- Framework for personalized learning with smart E-learning system using macro and micro adaptive approach
- A study of learners’ interactive preference on multimedia microlearning
- Personalised learning model for academic leveling and improvement in higher education
- Towards a Method to Predict the Evaluation Result in a Microlearning Context