Implementation of Machine Learning as Interactive Media in Modern Computer Learning
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Interactive Media, Machine Learning, Modern Computer
Abstract
Background. This study explores the use of machine learning as an interactive medium in the context of modern computer learning, driven by increasingly dynamic educational demands that require adaptive and personalized learning approaches.
Purpose. This study aims to evaluate the effectiveness of machine learning algorithms in developing interactive learning media that can improve conceptual understanding, active student participation, and a more personalized learning experience.
Method. The methodology used is qualitative with a case study and experimental design, including observations, interviews with instructors and students, and quantitative and qualitative data analysis of system interactions.
Results. The research findings indicate that learning media integrated with machine learning can adapt material to students' abilities and learning styles, provide real-time feedback, and increase student motivation and engagement in the learning process. The discussion highlights that machine learning functions not only as a technological tool but also as a means of pedagogical transformation that delivers an adaptive and personalized learning experience.
Conclusion. Thus, the implementation of machine learning as an interactive medium has proven effective in improving the quality of the teaching-learning process, encouraging active participant engagement, and adapting materials to individual needs, making it an important strategy in modern, responsive computer education.
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References
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