IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i17p9158-d625781.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/17/9158/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/17/9158/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Laura Cercenelli & Alessia De Stefano & Anna Maria Billi & Alessandra Ruggeri & Emanuela Marcelli & Claudio Marchetti & Lucia Manzoli & Stefano Ratti & Giovanni Badiali, 2022. "AEducaAR , Anatomical Education in Augmented Reality: A Pilot Experience of an Innovative Educational Tool Combining AR Technology and 3D Printing," IJERPH, MDPI, vol. 19(3), pages 1-14, January.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefan Hoffmann & Tom Joerß & Robert Mai & Payam Akbar, 2022. "Augmented reality-delivered product information at the point of sale: when information controllability backfires," Journal of the Academy of Marketing Science, Springer, vol. 50(4), pages 743-776, July.
    2. van de Sanden, Stephanie & Willems, Kim & Brengman, Malaika, 2022. "How customers motive attributions impact intentions to use an interactive kiosk in-store," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    3. Susitha, Emmanuel, 2021. "Influencing Factors of Employee Readiness to Adopt Advanced Manufacturing Technology (AMT) on Apparel Shop Floor in Sri Lanka," OSF Preprints puv7z_v1, Center for Open Science.
    4. El Barachi, May & Salim, Taghreed Abu & Nyadzayo, Munyaradzi W. & Mathew, Sujith & Badewi, Amgad & Amankwah-Amoah, Joseph, 2022. "The relationship between citizen readiness and the intention to continuously use smart city services: Mediating effects of satisfaction and discomfort," Technology in Society, Elsevier, vol. 71(C).
    5. Roshayati Abdul Hamid, 2022. "The Role of Employees’ Technology Readiness, Job Meaningfulness and Proactive Personality in Adaptive Performance," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    6. David A. Schweidel & Yakov Bart & J. Jeffrey Inman & Andrew T. Stephen & Barak Libai & Michelle Andrews & Ana Babić Rosario & Inyoung Chae & Zoey Chen & Daniella Kupor & Chiara Longoni & Felipe Thomaz, 2022. "How consumer digital signals are reshaping the customer journey," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1257-1276, November.
    7. Untung Rahardja & Shih-Chih Chen & Yu-Chun Lin & Tsung-Chieh Tsai & Qurotul Aini & Asif Khan & Fitra Putri Oganda & Elisa Royani Dewi & Ying-Chieh Cho & Chung-Hao Hsu, 2023. "Evaluating the Mediating Mechanism of Perceived Trust and Risk toward Cryptocurrency: An Empirical Research," SAGE Open, , vol. 13(4), pages 21582440231, December.
    8. Wu, Ruijuan & Li, Peiyu, 2023. "Continuance intention to use self-delivery boxes: An empirical study in Tianjin, China," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    9. Markus Blut & Cheng Wang & Nancy V. Wünderlich & Christian Brock, 2021. "Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 632-658, July.
    10. Yumi Yi & Rosemary Hyejin Moon, 2021. "Sustained Use of Virtual Meeting Platforms for Classes in the Post-Coronavirus Era: The Mediating Effects of Technology Readiness and Social Presence," Sustainability, MDPI, vol. 13(15), pages 1-14, July.
    11. Achraf Othman & Amnah Al Mutawaa & Amani Al Tamimi & Maha Al Mansouri, 2023. "Assessing the Readiness of Government and Semi-Government Institutions in Qatar for Inclusive and Sustainable ICT Accessibility: Introducing the MARSAD Tool," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    12. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    13. Huang, Duen-Huang & Chueh, Hao-En, 2022. "Usage intention model of mobile apps in membership application," Journal of Business Research, Elsevier, vol. 139(C), pages 1255-1260.
    14. Jacobs, Nele & Swoboda, Bernhard, 2023. "The role of national institutions in the effects of consumers’ perceived customer orientation and firm innovativeness," International Business Review, Elsevier, vol. 32(5).
    15. Maier, Erik & Bornschein, Rico & Manss, Rico & Hesse, Damian, 2023. "Financial consequences of adding bricks to clicks," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 609-628.
    16. Jan, Ihsan Ullah & Ji, Seonggoo & Kim, Changju, 2023. "What (de) motivates customers to use AI-powered conversational agents for shopping? The extended behavioral reasoning perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    17. Polyakova, V. & Streltsova, E. & Iudin, I. & Kuzina, L., 2024. "Irreversible effects? How the digitalization of daily practices has changed after the COVID-19 pandemic," Technology in Society, Elsevier, vol. 76(C).
    18. Aiste Dirzyte & Živilė Sederevičiūtė-Pačiauskienė & Jolita Šliogerienė & Aivaras Vijaikis & Aidas Perminas & Lukas Kaminskis & Giedrius Žebrauskas & Kęstutis Mačiulaitis, 2021. "Peer-to-Peer Confirmation, Positive Automatic Thoughts, and Flourishing of Computer Programming E-Learners," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    19. Susitha, Emmanuel, 2021. "Influencing Factors of Employee Readiness to Adopt Advanced Manufacturing Technology (AMT) on Apparel Shop Floor in Sri Lanka," OSF Preprints puv7z, Center for Open Science.
    20. Elodie Attié & Lars Meyer-Waarden, 2022. "The acceptance and usage of smart connected objects according to adoption stages: an enhanced technology acceptance model integrating the diffusion of innovation, uses and gratification and privacy ca," Post-Print hal-04065165, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9158-:d:625781. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.