Abstract
This study examines personalized learning within smart education to address the diverse needs of both fast and slow learners. Traditional teaching methods frequently neglect individual learning styles, resulting in disengagement and unequal academic performance. In this study, we provide customized learning materials and support to participants. Following a preliminary quiz that categorizes learners into distinct groups, personalized YouTube video tutorials are made available. Continuous internal assessments monitor progress, ensuring that learners receive the necessary support. A comprehensive course test concludes the evaluation process. Our analysis reveals significant performance disparities between slow and fast learners, highlighting the effectiveness of personalized learning in smart education. Slow learners demonstrate improved comprehension and retention, while fast learners excel with more challenging content. This underscores the importance of tailoring educational approaches to meet diverse learner needs, thereby promoting equitable learning outcomes in smart education environments.
References
- An, SangJin, Eunkyoung Lee, and YoungJun Lee. "A Comparative Study of E-Learning System for Smart Education." International Association for Development of the Information Society (2013).
- Hutt, Stephen, Jaclyn Ocumpaugh, Juliana Ma, Alexandra L. Andres, Nigel Bosch, Luc Paquette, Gautam Biswas, and Ryan S. Baker. "Investigating SMART Models of Self-Regulation and Their Impact on Learning." International Educational Data Mining Society (2021).
- Radosavljevic, Vitomir, Slavica Radosavljevic, and Gordana Jelic. "A model of adaptive learning in smart classrooms based on the learning strategies." PEOPLE Int. J. Soc. Sci 5 (2019): 662-679.
- Ilkou, Eleni, and Beat Signer. "A Technology-enhanced Smart Learning Environment based on the Combination of Knowledge Graphs and Learning Paths." In CSEDU (2), 2020, 461-468.
- Walkington, Candace A. "Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes." Journal of educational psychology 105, no. 4 (2013): 932.
- Palanivel, K. "Emerging technologies to smart education." Int. J. Comput. Trends Technol 68, no. 2 (2020): 5-16.
- Chen, Chia-Chen, Patrick Hung, Erol Eğrioğlu, and Kuo-Lun Hsiao, eds. Deep Learning in Adaptive Learning: Educational Behavior and Strategy. Frontiers Media SA, 2022.
- Kaushik, Dr Madhu Bala. "Adaptive Learning: AI's Journey in Education and Beyond." Educational Administration: Theory And Practice, 30 (1), 2875-2880. (2024