Customized learning strategies for students

Abstract: Effective learning results in modern educational environments increasingly depend on attending to students' different learning preferences and styles. This study describes a complete methodology that builds personalized learning methods for individual students by fusing state-of-the-art machine learning algorithms with well-established educational frameworks. In particular, we suggest combining the VARK model, Glove embedding, and the Long Short-Term Memory (LSTM) method to create a strong foundation for individualized instruction. The VARK approach divides students into four learning styles: kinesthetic, visual, auditory, and reading/writing.

Keywords: Personalized learning, machine learning, LSTM algorithm, VARK model, Glove embedding, learning styles, text classification.