Enhancing Student Well-Being Prediction with an Innovative Attention-LSTM Model

• Published in: International journal of advanced computer science & applications 2024-01, Vol.15 (9)
• Main Authors: Waiker, Vinod, Ramesh, Janjhyam Venkata Naga, Kiran, Ajmeera, Jangir, Pradeep, Haldar, Ritwik, Babu, Padamata Ramesh, Thenmozhi, E.
• Format: Article
• Language: English
• Subjects: Accuracy; Adolescents; Algorithms; Artificial intelligence; College students; Colleges & universities; Computer science; Computers; Datasets; Engineering; Fitness; Learning; Mental health; Neural networks; Performance prediction; Social networks; Stress
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150972

This study introduces a groundbreaking method for predicting scholar well-being with the use of a sophisticated interest-primarily based Long Short-Term Memory (LSTM) version. Addressing the developing problem of intellectual health in academic settings, the studies pursuits to provide new insights and powerful techniques for reinforcing pupil mental well-being. The recognition is on enhancing the prediction of mental fitness issues via the revolutionary use of interest-primarily based LSTM algorithms, which excel in discerning various ranges of relevance among input facts points. The version leverages a unique methodology to procedure various datasets, which include academic information, social media activity, and textual survey responses. By emphasizing sizable capabilities like language patterns and shifts in educational performance, the attention-based totally LSTM version overcomes barriers of conventional predictive techniques and demonstrates superior accuracy in figuring out subtle indicators of mental health troubles. The schooling dataset is categorized into behavioral states along with "healthy," "confused," "traumatic," and "depressed," allowing the version to build a strong learning foundation. This research highlights the transformative ability of superior interest-primarily based strategies, offering an effective device for improving our know-how and predictive capabilities concerning adolescent mental fitness situations. The study underscores the significance of integrating progressive device studying tactics in addressing intellectual health demanding situations and enhancing standard scholar well-being. Upon implementation and rigorous checking out in Python, the proposed technique achieves a notable accuracy price of 98.9% in identifying mental fitness issues among college students. This observe underscores the transformative potential of superior interest-based totally strategies, thereby improving the expertise and predictive competencies concerning mental fitness conditions in teens.