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.
Cite:
Dr.M.Srinivasa Sesha Sai, Lokireddy Nagalakshmi, Mandadi Poojitha,Lingala Keerthana, LellaVenkata Kavya, "Customized learning strategies for students", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13375.
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DOI:
10.17148/IJARCCE.2024.13375
