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Associations between Depression, Anxiety, Fatigue, and Learning Motivating Factors in e-Learning-Based Computer Programming Education

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  • Aiste Dirzyte

    (Faculty of Creative Industries, Vilnius Gediminas Technical University, 10221 Vilnius, Lithuania
    Institute of Psychology, Mykolas Romeris University, 08303 Vilnius, Lithuania)

  • Aivaras Vijaikis

    (Institute of Psychology, Mykolas Romeris University, 08303 Vilnius, Lithuania)

  • Aidas Perminas

    (Department of Psychology, Vytautas Magnus University, 44248 Kaunas, Lithuania)

  • Romualda Rimasiute-Knabikiene

    (Institute of Psychology, Mykolas Romeris University, 08303 Vilnius, Lithuania)

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.

Suggested Citation

  • Aiste Dirzyte & Aivaras Vijaikis & Aidas Perminas & Romualda Rimasiute-Knabikiene, 2021. "Associations between Depression, Anxiety, Fatigue, and Learning Motivating Factors in e-Learning-Based Computer Programming Education," IJERPH, MDPI, vol. 18(17), pages 1-31, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9158-:d:625781
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    References listed on IDEAS

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    1. Markus Blut & Cheng Wang, 2020. "Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage," Journal of the Academy of Marketing Science, Springer, vol. 48(4), pages 649-669, July.
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    2. Maria Clelia Zurlo & Federica Vallone & Maria Francesca Cattaneo Della Volta, 2022. "Perceived Past and Current COVID-19-Stressors, Coping Strategies and Psychological Health among University Students: A Mediated-Moderated Model," IJERPH, MDPI, vol. 19(16), pages 1-19, August.
    3. Changbae Mun & Hyodong Ha, 2022. "A Theoretical Framework for Analyzing Student Achievement in Software Education," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    4. Jian Zhao & Elaine Chapman & Stephen Houghton, 2022. "Key Predictive Factors in the Mental Health of Chinese University Students at Home and Abroad," IJERPH, MDPI, vol. 19(23), pages 1-15, December.

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