Identification of Latent Profiles and Determining Factors of Academic Stress in University Students: An Integrated Unsupervised–Supervised Machine Learning Approach
Academic stress is one of the main challenges affecting the psychological well-being of university students due to its impact on mental health, academic performance, and quality of life. The aim of this study was to analyze and model the factors associated with academic stress by integrating unsuper...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2026
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| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| _version_ | 1881700506113409024 |
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| author | Miguel Angel Valles-Coral Richard Injante Lloy Pinedo Juan Rafael Juárez Díaz Wilson Torres-Delgado Danny Lévano Rodríguez Job Alberto Saavedra-Saavedra Cecilia Edith García Rivas Plata Roel Dante Gómez Apaza María García-Paredes |
| author_browse | Cecilia Edith García Rivas Plata Danny Lévano Rodríguez Job Alberto Saavedra-Saavedra Juan Rafael Juárez Díaz Lloy Pinedo María García-Paredes Miguel Angel Valles-Coral Richard Injante Roel Dante Gómez Apaza Wilson Torres-Delgado |
| author_facet | Miguel Angel Valles-Coral Richard Injante Lloy Pinedo Juan Rafael Juárez Díaz Wilson Torres-Delgado Danny Lévano Rodríguez Job Alberto Saavedra-Saavedra Cecilia Edith García Rivas Plata Roel Dante Gómez Apaza María García-Paredes |
| author_sort | Miguel Angel Valles-Coral |
| collection | Producción científica |
| description | Academic stress is one of the main challenges affecting the psychological well-being of university students due to its impact on mental health, academic performance, and quality of life. The aim of this study was to analyze and model the factors associated with academic stress by integrating unsupervised and supervised machine learning techniques. The study was conducted with a sample of 605 students from the Universidad Nacional de San Martín (Peru), who completed validated psychometric instruments, including the PSS-10, LASSI, MBI-SS, PSQI, and A-CEA. In the first stage, dimensionality reduction and clustering techniques were applied to identify latent profiles, resulting in four distinct groups reflecting different levels of adaptation and psychological vulnerability. In the second stage, eight supervised regression models were evaluated: Linear Regression, Ridge, Lasso, Elastic Net, Random Forest, Gradient Boosting, XGBoost, and CatBoost. Lasso and Elastic Net showed virtually equivalent performance, achieving coefficients of determination (R2) close to 0.61 on the independent test set. Variable importance analysis revealed that academic burnout, sleep quality, and coping strategies were the main factors associated with perceived stress, together with contextual variables with lower relative importance. Overall, the results confirm the multidimensional nature of academic stress and show that integrating unsupervised and supervised approaches provides a more comprehensive understanding of the phenomenon in university settings. |
| format | Article |
| id | openalex:W7162568305 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | openalex:W7162568305Identification of Latent Profiles and Determining Factors of Academic Stress in University Students: An Integrated Unsupervised–Supervised Machine Learning ApproachMiguel Angel Valles-CoralRichard InjanteLloy PinedoJuan Rafael Juárez DíazWilson Torres-DelgadoDanny Lévano RodríguezJob Alberto Saavedra-SaavedraCecilia Edith García Rivas PlataRoel Dante Gómez ApazaMaría García-Paredesacademic stressuniversity studentslatent profilesacademic burnoutsleep qualitycoping strategiespsychological vulnerabilityperceived stresspsychological well-beinglassiPSS-10Pittsburgh Sleep Quality IndexLASSOelastic netrandom forestgradient boostingXGBoostCatBoostfeature importanceStudent Stress and CopingStress and Burnout ResearchOnline Learning and AnalyticsAcademic stress is one of the main challenges affecting the psychological well-being of university students due to its impact on mental health, academic performance, and quality of life. The aim of this study was to analyze and model the factors associated with academic stress by integrating unsupervised and supervised machine learning techniques. The study was conducted with a sample of 605 students from the Universidad Nacional de San Martín (Peru), who completed validated psychometric instruments, including the PSS-10, LASSI, MBI-SS, PSQI, and A-CEA. In the first stage, dimensionality reduction and clustering techniques were applied to identify latent profiles, resulting in four distinct groups reflecting different levels of adaptation and psychological vulnerability. In the second stage, eight supervised regression models were evaluated: Linear Regression, Ridge, Lasso, Elastic Net, Random Forest, Gradient Boosting, XGBoost, and CatBoost. Lasso and Elastic Net showed virtually equivalent performance, achieving coefficients of determination (R2) close to 0.61 on the independent test set. Variable importance analysis revealed that academic burnout, sleep quality, and coping strategies were the main factors associated with perceived stress, together with contextual variables with lower relative importance. Overall, the results confirm the multidimensional nature of academic stress and show that integrating unsupervised and supervised approaches provides a more comprehensive understanding of the phenomenon in university settings.Articlehttps://doi.org/10.3390/data11060129W7162568305enData2306-57292026-05-27 |
| spellingShingle | academic stress university students latent profiles academic burnout sleep quality coping strategies psychological vulnerability perceived stress psychological well-being lassi PSS-10 Pittsburgh Sleep Quality Index LASSO elastic net random forest gradient boosting XGBoost CatBoost feature importance Student Stress and Coping Stress and Burnout Research Online Learning and Analytics Miguel Angel Valles-Coral Richard Injante Lloy Pinedo Juan Rafael Juárez Díaz Wilson Torres-Delgado Danny Lévano Rodríguez Job Alberto Saavedra-Saavedra Cecilia Edith García Rivas Plata Roel Dante Gómez Apaza María García-Paredes Identification of Latent Profiles and Determining Factors of Academic Stress in University Students: An Integrated Unsupervised–Supervised Machine Learning Approach |
| title | Identification of Latent Profiles and Determining Factors of Academic Stress in University Students: An Integrated Unsupervised–Supervised Machine Learning Approach |
| title_full | Identification of Latent Profiles and Determining Factors of Academic Stress in University Students: An Integrated Unsupervised–Supervised Machine Learning Approach |
| title_fullStr | Identification of Latent Profiles and Determining Factors of Academic Stress in University Students: An Integrated Unsupervised–Supervised Machine Learning Approach |
| title_full_unstemmed | Identification of Latent Profiles and Determining Factors of Academic Stress in University Students: An Integrated Unsupervised–Supervised Machine Learning Approach |
| title_short | Identification of Latent Profiles and Determining Factors of Academic Stress in University Students: An Integrated Unsupervised–Supervised Machine Learning Approach |
| title_sort | identification of latent profiles and determining factors of academic stress in university students an integrated unsupervised supervised machine learning approach |
| topic | academic stress university students latent profiles academic burnout sleep quality coping strategies psychological vulnerability perceived stress psychological well-being lassi PSS-10 Pittsburgh Sleep Quality Index LASSO elastic net random forest gradient boosting XGBoost CatBoost feature importance Student Stress and Coping Stress and Burnout Research Online Learning and Analytics |
| topic_facet | academic stress university students latent profiles academic burnout sleep quality coping strategies psychological vulnerability perceived stress psychological well-being lassi PSS-10 Pittsburgh Sleep Quality Index LASSO elastic net random forest gradient boosting XGBoost CatBoost feature importance Student Stress and Coping Stress and Burnout Research Online Learning and Analytics |
| url | https://doi.org/10.3390/data11060129 |
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