Learning Management Systems (LMS) play a critical role in modern education by organizing content, facilitating communication, and supporting student assessment. However, most current LMS platforms depend on manual grading and generalized feedback, which can be inefficient and lack personalization. This research enhances LMS capabilities by integrating deep learning techniques—specifically Natural Language Processing (NLP)—to automate assessment and deliver personalized feedback. The system analyzes student input, such as written assignments and discussion forum posts, to evaluate performance and generate real-time, adaptive feedback. A modular framework was developed using a Bidirectional LSTM-based architecture trained on sequence data with regression objectives. The model was evaluated using the Mean Squared Error (MSE) metric. The results show that the model performs reasonably well, with predictions closely aligned to actual values in most cases, although its performance decreases slightly at the distribution extremes. Visualization via scatter plots further confirms the model's ability to capture context and structure in textual input. These findings demonstrate the model's feasibility in educational environments and its potential to reduce instructor workload while improving the quality of feedback. Future work will consider integrating attention mechanisms and multilingual capabilities for broader applicability.
Kusuma et al. (Thu,) studied this question.