Personalized Learning Strategies in Smart Higher Education: A Systematic Review with Thematic Qualitative Analysis
Keywords:
Personalized learning, Smart higher education, Adaptive learning, Thematic analysis, Artificial intelligence, Learning analyticsAbstract
This study aims to identify and analyze the key strategies of personalized learning within the context of smart higher education using qualitative thematic analysis. This research is a systematic review with a qualitative approach based on thematic analysis. Data were collected through a structured literature review of 12 peer-reviewed articles from databases such as Scopus, Web of Science, and Google Scholar. Articles were screened and selected based on relevance and quality criteria. The selected texts were analyzed using NVivo 14 software. The coding process continued until theoretical saturation was reached. The thematic analysis revealed three main themes: (1) Technological design of personalized learning, (2) Learner-centered instructional strategies, and (3) Management and evaluation of personalized learning in higher education. Each theme included several subthemes and key concepts related to the design, implementation, and assessment of personalized learning in smart university settings. The findings highlight the role of artificial intelligence, adaptive systems, self-directed learning, data-driven feedback, and smart policymaking. Personalized learning in smart higher education is a multifaceted strategy requiring the integration of advanced technologies, learner-centered approaches, and intelligent management systems. The insights from this study offer theoretical and practical guidance for developing and implementing effective personalized learning solutions in forward-thinking academic institutions.
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