A review of the Development Trend of Personalized learning Technologies and its Applications
| Autor: | Raiton Malema Ambele, Shubi Felix Kaijage, Mussa Ally Dida, Lena Trojer, Newton M. Kyando |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
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| Zdroj: | International Journal of Advances in Scientific Research and Engineering (IJASRE), ISSN:2454-8006, DOI: 10.31695/IJASRE; Vol. 8 No. 11: November-2022; 75-91 International Journal of Advances in Scientific Research and Engineering; 8(11), pp 75-91 (2022) |
| ISSN: | 2454-8006 |
| Popis: | Personalized learning tailors material and strategy to student requirements, interests, and goals in e-learning. These developments help educational institutions and other organizations to keep up with the fast pace of information technology, communications, and computing power. Studies show that self-adaptive learning and relevant learning information improve study efficiency.Compared to traditional teaching methods, the practice of online education is well in its infancy. On the other hand, the pedagogy and evaluation of students in online courses have a large gap that has to be filled, necessitating significant improvements in e-learning. We call this approach to education "personalized learning," which is a central focus of today's leading online education platforms. Several studies have been conducted on e-learning and personalized learning, but few investigated the development trend of personalized learning technologies and applications. Therefore this study examines the literature to close the gap and promote the development trend for personalized learning technologies and applications in higher education from 2010 to 2021 by analyzing related journal articles. The pivotal studies used inclusion criteria after a searchgenerated 372 complete research articles and reduced them to 146 publications based on their proposed learning domains and research themes. Through carefully reviewing current trends and successes in numerous aspects of personalized learning, this discussion analyzes prospective future research directions in the field of personalized learning. |
| Databáze: | OpenAIRE |
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