Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/144884
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Psichologija / Psychology (S006)
Author(s): Diržytė, Aistė;Vijaikis, Aivaras;Perminas, Aidas;Rimasiute-Knabikienė, Romualda
Title: Associations between depression, anxiety, fatigue, and learning motivating factors in e-learning-based computer programming education
Is part of: International journal of environmental research and public health. Basel : MDPI, 2021, vol. 18, iss. 17
Extent: p. 1-31
Date: 2021
Keywords: Depression;Anxiety;Fatigue;Learning;Motivating factors
Abstract: Quarantines imposed due to COVID-19 have forced the rapid implementation of e-learning, but also increased the rates of anxiety, depression, and fatigue, which relate to dramatically diminished e-learning motivation. Thus, it was deemed significant to identify e-learning motivating factors related to mental health. Furthermore, because computer programming skills are among the core competencies that professionals are expected to possess in the era of rapid technology development, it was also considered important to identify the factors relating to computer programming learning. Thus, this study applied the Learning Motivating Factors Questionnaire, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder Scale-7 (GAD-7), and the Multidimensional Fatigue Inventory-20 (MFI-20) instruments. The sample consisted of 444 e-learners, including 189 computer programming e-learners. The results revealed that higher scores of individual attitude and expectation, challenging goals, clear direction, social pressure, and competition significantly varied across depression categories. The scores of challenging goals, and social pressure and competition, significantly varied across anxiety categories. The scores of individual attitude and expectation, challenging goals, and social pressure and competition significantly varied across general fatigue categories. In the group of computer programming e-learners: challenging goals predicted decreased anxiety; clear direction and challenging goals predicted decreased depression; individual attitude and expectation predicted diminished general fatigue; and challenging goals and punishment predicted diminished mental fatigue. Challenging goals statistically significantly predicted lower mental fatigue, and mental fatigue statistically significantly predicted depression and anxiety in both sample groups
Internet: https://www.vdu.lt/cris/bitstream/20.500.12259/144884/1/ISSN1661-7827_2021_V_18_17.PG_1-31.pdf
https://hdl.handle.net/20.500.12259/144884
https://doi.org/10.3390/ijerph18179158
Affiliation(s): Mykolo Romerio universitetas
Psichologijos katedra
Vilniaus Gedimino technikos universitetas
Vytauto Didžiojo universitetas
Appears in Collections:1. Straipsniai / Articles
Universiteto mokslo publikacijos / University Research Publications

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