Autor(es): Azevedo, Beatriz Flamia ; Bezerra, Ana J.B. ; Sirmakessis, Spiros ; Pereira, Ana I.
Data: 2026
Identificador Persistente: http://hdl.handle.net/10198/35175
Origem: Biblioteca Digital do IPB
Assunto(s): Wellness; Student performance; Stress level; Data analysis; Machine learning; Clustering
Balancing academic demands with personal and professional responsibilities has become an increasingly challenging task, making it difficult to maintain well-being and potentially leading to serious health problems. The stress resulting from these multiple daily tasks, combined with the pressure to perform at high academic levels, directly impacts students’ mental and emotional health, significantly compromising their quality of life. In this work, statistical and clustering techniques are employed to analyze the dataset “Daily Lifestyle and Academic Performance of Students”. The objective of this work is to explore the relationship between students’ daily habits, level of stress, and the impact on academic performance. The results point out that many students have difficulty managing time and maintaining well-being (low-stress levels) with high academic performance since, according to the results, the higher the academic outcome, the higher the student’s stress level.
