Potential Abstract:
Individualized education is crucial for meeting the diverse needs of learners in today’s classrooms. This study explores the use of machine learning algorithms within an educational portal framework to enhance individualized learning experiences for students. By leveraging personalized learning frames, this research seeks to expand the capabilities of educational technology to better support the unique needs of each learner. The study examines the effectiveness of using machine learning models to analyze student data and provide personalized recommendations for instructional strategies, resources, and interventions. Additionally, the research investigates the impact of integrating these personalized recommendations within an educational portal on student engagement, motivation, and academic outcomes. Through a mixed-methods approach, including quantitative analysis of student performance data and qualitative examination of student and teacher perceptions, this study aims to provide insights into the potential benefits and challenges of implementing machine learning technologies in educational settings. By merging advances in artificial intelligence with principles of effective teaching and learning, this study contributes to the growing body of research on individualized education and technology integration in the classroom.
Potential References:
- DIGITAL SUPPORT SYSTEM FOR FULL-TIME AND REMOTE MASS INDIVIDUALIZED EDUCATION WITH ELEMENTS OF MACHINE LEARNING
- Machine teaching: An explainable machine learning model for individualized education
- Individualizing learning using intelligent technology and universally designed curriculum
- Personalized education in the artificial intelligence era: what to expect next
- Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways
