Application of deep learning-based personalized learning path prediction and resource recommendation for inheriting scientist spirit in graduate education

This study explores the application of artificial intelligence (AI) and deep learning (DL) technologies in graduate education to promote the inheritance and development of the scientist spirit. This study employs a Long Short-Term Memory (LSTM) network to predict students' learning paths. Meanwhile, it constructs a DL-based personalized learning path and resource recommendation model by integrating a hybrid recommendation mechanism combining collaborative filtering and content-based filtering. The model inputs students' historical learning data and utilizes LSTM to capture long-term dependencies for predicting future learning activities. At the same time, it dynamically adjusts the learning rate through a reinforcement learning mechanism to optimize model performance. Additionally, this study introduces the Local Interpretable Model-Agnostic Explanations (LIME) algorithm to enhance the model's interpretability, ensuring that educators can understand the model's decision-making logic. Model training employs cross-validation techniques, and Principal Component Analysis (PCA) is used for dimensionality reduction and feature selection to improve data processing efficiency. Experimental results demonstrate that the DL model significantly outperforms traditional models in personalized learning path prediction, resource matching efficiency, and student performance prediction. Particularly, the DL model has an accuracy of 92.5%, an F1 score of 91.8%, an Area Under the Receiver Operating Characteristic Curve value of 0.95, a user satisfaction rate of 89.2%, and a prediction bias of only -0.75%. Furthermore, through user satisfaction surveys and expert reviews, this study qualitatively analyzes the impact of AI and DL technologies on educational practices. This confirms their value in enhancing education quality and fostering a scientist spirit. The study concludes that AI and DL technologies can effectively optimize graduate education models and promote the inheritance of the scientist spirit. Moreover, these technologies can cultivate innovative capabilities and provide theoretical support and practical guidance for intelligent educational reform.