Prediction of Students' COVID-19 Resilience: An Artificial Neural Network Based on Gender, Age, Stress Intensity, and Mindfulness Variables

Autor/inn/en	Nursalim, Mochamad; Saroinsong, Wulan P.; Boonroungrut, Chinun; Wagino; Costa, Augusto da
Titel	Prediction of Students' COVID-19 Resilience: An Artificial Neural Network Based on Gender, Age, Stress Intensity, and Mindfulness Variables
Quelle	In: Psychology in the Schools, 60 (2023) 9, S.3253-3265 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Saroinsong, Wulan P.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3085
DOI	10.1002/pits.22920
Schlagwörter	Prediction; COVID-19; Pandemics; Resilience (Psychology); Gender Differences; Age Differences; Stress Variables; Metacognition; Artificial Intelligence; Predictor Variables + Suchen Sie Ihr Suchwort? Vorhersage; Geschlechterkonflikt; Age; Difference; Age difference; Altersunterschied; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Künstliche Intelligenz; Prädiktor
Abstract	The global health emergency, COVID-19, significantly influenced schooling in Indonesia. Students employed a variety of coping mechanisms to cope with unusual stress levels during confinement time. Hence, as students' COVID-19 resilience, investigation, and prevention were required for high and chronic stress connected with various disorders. This study aimed to design a predictive model of students' COVID-19 resilience based on artificial intelligence that included certain demographic variables, stress intensity, and mindfulness and to study the relationship between them. A total of 6580 Indonesian students were involved in this study (57.9% female and 70.3% aged between 13 and 15 years old). The prediction model was performed by the architecture of artificial neural networks. The results showed that the model's predictive capacity was over 63% in the testing phase, then reached almost 65% in the holdout phase. Students' COVID-19 resilience was mainly predicted by stress intensity and mindfulness with 100% and 40.9% normalized importance values, respectively. Receiver operating characteristic curve assessed and remarked the model as more superior than random. Our research gave some insight into the use of artificial intelligence in educational research to predict psychological variables. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01