MODEL AGNOSTIC META LEARNING – LONG SHORT-TERM MEMORY FOR LEARNING STYLE CLASSIFICATION

An adaptive learning system aims to enhance the effectiveness of the educational process by tailoring it to individual students. A key aspect of this adaptation involves identifying the most suitable learning approach, based on Visual, Auditory, and Kinesthetic (VAK) learning styles. However, accurately classifying learning styles remains a challenge due to the presence of concept drift, which affects the network’s ability to generalize across different learners. To address this, the Model Agnostic Meta Learning – Long Short-Term Memory (MAML-LSTM) model is proposed in this research study for effective learning style classification. MAML is incorporated into the LSTM network to identify shifts in classification patterns and adapt to new learners quickly. Rather than retraining the network from the beginning, the model dynamically fine-tunes the LSTM in response to concept drift, thereby improving its generalization capability. The MAML-LSTM integration enables rapid adaptation to concept drift by fine-tuning on limited new data, eliminating the need for complete retraining. This enhances the model’s ability to maintain high classification accuracy across dynamic learner behaviors. Additionally, Local Interpretable Model-agnostic Explanations (LIME) are employed after classification to highlight key features, ensuring greater transparency and interpretability. The proposed MAML-LSTM achieves 97.77% accuracy, 97.72% precision, 97.72% recall, 97.72% F1-score, 97.72% specificity, and 99.81% AUC on the VAK learning style dataset, outperforming existing algorithms.