Human-Centered Context-Aware Student Stress Detection Using LSTM- Based Academic Behavior Analytics
Keywords:
Deep learning, LSTM neural network, Natural Language Processing, Sentiment classification, Student stress detectionAbstract
Academic stress has become a major concern among students in modern educational environments. Increasing academic pressure, frequent examinations, tight assignment deadlines, and the extensive use of digital learning platforms contribute significantly to students’ mental stress. If not identified at an early stage, prolonged stress can negatively affect academic performance, emotional well-being, and overall student development. Therefore, early detection of stress is essential for providing timely academic and psychological support. This study proposes a human-centered and implementation-oriented framework for detecting student stress using sentiment analysis and deep learning techniques. The proposed system utilizes a Long Short-Term Memory (LSTM) neural network to analyze textual feedback collected from students through surveys, discussion forums, and online learning platforms. The collected text data undergo several preprocessing steps, including cleaning, tokenization, stopword removal, and sequence padding, to improve data quality and model learning capability. The LSTM model is designed to capture contextual and sequential patterns present in student expressions, enabling accurate classification of text into stress and non-stress categories. Experimental evaluation demonstrates that the proposed approach achieves reliable performance in identifying stress-related sentiments from student feedback. The system provides a practical tool for educational institutions to monitor student well-being and implement early interventions. Overall, the proposed method highlights the potential of deep learning and sentiment analysis in supporting student mental health within academic environments.
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