article · Zenodo (CERN European Organization for Nuclear Research)
The necessary adjustments the objective of this study is to examine how students studying educational technology develop their analytical thinking and computer maintenance skills in an adaptive learning environment by interacting with their learning style (deep/surface) and content presentation mode (conditional/flexible). The necessity to create electronic learning environments that take into account the unique characteristics of each student and modify content in accordance with their cognitive levels and learning styles led to the study. A 2×2 factorial experimental design was used in the study, which involved 56 first-year students from Kafrelsheikh University's Department of Educational Technology, Faculty of Specific Education. When compared to the surface learning style, the results showed statistically significant differences favoring the deep learning style in the development of computer maintenance abilities and analytical thinking. Furthermore, in terms of improving students' overall performance, the flexible material presentation option fared better than the conditional method. Since students with a deep learning style who studied using flexible content attained the highest levels of analytical thinking and practical abilities, the results also showed a strong relationship between learning style and content presentation manner. These findings highlight how crucial it is to use adaptive learning environments in higher education since they offer individualized learning experiences that take into consideration students' learning preferences and foster their independence and drive to study. Additionally, to attain more profound and long-lasting learning outcomes, adaptive and AI-based technologies can be included into contemporary teaching methodologies.
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DOI: 10.5281/zenodo.18422006
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