An analysis of personalized learning platform model

Abstract

In the modern continuously developing field of education, it is concluded that the establishment of individual learning appears to be possible with the help of machine learning solutions. The case being presented in this paper calls for the implementation of a concept of a learning platform that is packaged with a state-of-the-art machine learning tool set to boost academic achievement. The proposed method consists of three main components: This paper presents a seven-feature approach that includes detailed response and feedback, dynamic control of learning processes, and a recommendation system. The recommendation system applies information, demographic and collaborative information about learning resources to each learner based on individual learning style and academic accomplishment records.

Adaptive learning thus self-organizes content based on the various aspects of student interaction and achievement so that a perfect learning path is achieved. Feedback as motivation and constant improvement, feedback for timely, useful, and individualized criticism through sentiment and Natural language processing. The integration of these components, however, suggests the potential for developing the present recommendation platform to offer a more productive and enjoyable educational experience that meets the needs of every learner. This outsiders’ concept seems to have the potential to outcompete traditional normative pedagogy teaching models, in the sense of efficacy and effectiveness as depicted by learners’ performance and satisfaction.