Personalized learning path planning for higher education based on deep generative models and quantum machine learning: a multimodal learning analysis method integrating transformer, adversarial training and quantum state classification

In the field of personalized learning path planning for higher education, traditional methods lack in-depth analysis of students' dynamically changing learning status and interests, resulting in insufficient personalization, and focus on the analysis of a single data type, making it difficult to integrate data of different modalities. This paper proposes a multimodal learning analysis method for personalized learning path planning in higher education, addressing the limitations of traditional methods that do not account for dynamic changes in students' learning behaviors and interests. The approach integrates Transformer models, adversarial training, and quantum state classification to analyze multimodal data, including text, audio, and video, to capture learning patterns. The Transformer model uses a self-attention mechanism to generate personalized learning paths based on integrated data. Adversarial training is applied to simulate abnormal data and enhance the model's robustness to various learning scenarios. Quantum state classification improves data processing efficiency, addressing challenges in handling high-dimensional multimodal data. Experimental results show that the Transformer model achieves stable accuracy of 0.95 and recall of 0.91 for personalized learning path generation. The adversarial training method reduces the loss value to around 0.05, while the introduction of quantum state classification reduces processing time to 56 s in the 18th round, a 31-s improvement. These results confirm the effectiveness of the proposed method in enhancing the accuracy, robustness, and computational efficiency of personalized learning path generation in higher education.