Modeling Learner Memory Based on LSTM Autoencoder and Collaborative Filtering

Memory modeling, aimed at predicting the memory states of learners throughout their learning process, has become an integral component in online learning systems. This is particularly crucial for applications such as spaced repetition scheduling and knowledge tracking. However, existing machine learning-based memory modeling methods encounter challenges with weak supervision signals and sparse interaction data. To tackle these issues, we propose a novel approach named LSTM Autoencoder Collaborative Filtering (LACF) for modeling learner memory. Our model utilizes an LSTM autoencoder to extract temporal features from user-item interaction sequences. Additionally, a collaborative filtering module, based on the deepFM architecture, is employed to effectively learn interactions between different features, facilitating comprehensive low-order and high-order feature combinations. Experimental results demonstrate that LACF significantly outperforms existing state-of-the-art memory modeling methods, offering enhanced accuracy and precision in predictions.