Associations between Depression, Anxiety, Fatigue, and Learning Motivating Factors in e-Learning-Based Computer Programming Education
Abstract
:1. Introduction
1.1. Learning Motivating Factors
1.2. Learning and Emotional Health
1.3. Anxiety, Depression, and Fatigue
1.4. Mental Health and e-Learning Education
2. Materials and Methods
2.1. Sample
2.2. Instruments
2.2.1. The PHQ-9
2.2.2. The GAD-7
2.2.3. The MFI-20
2.2.4. The Learning Motivating Factors Questionnaire
2.3. Statistical Analysis
3. Results
4. Discussion
4.1. Lower Scores of Depression Partially Relate to Higher Scores of Learning Motivating Factors
4.2. Lower Anxiety Scores Partially Relate to Higher Scores of Intrinsic Learning Motivating Factors
4.3. Lower Scores of General Fatigue Partially Relate to Higher Scores of Learning Motivating Factors
4.4. Learning Motivating Factors Partially Predict Anxiety Level
4.5. Learning Motivating Factors Partially Predict Depression Level
4.6. Learning Motivating Factors, Anxiety, and Depression Partially Predict Fatigue
4.7. Associations between the Study Variables Partially Differ in the Compared Groups
4.8. Limitations and Future Directions
4.9. Theoretical Implications
4.10. Practical Implications
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Blut, M.; Wang, C. Technology readiness: A meta-analysis of conceptualizations of the construct and its impact on technology usage. J. Acad. Mark. Sci. 2020, 48, 649–669. [Google Scholar] [CrossRef] [Green Version]
- Scherer, R.; Siddiq, F.; Sánchez Viveros, B. The cognitive benefits of learning computer programming: A meta-analysis of transfer effects. J. Educ. Psychol. 2019, 111, 764–792. [Google Scholar] [CrossRef] [Green Version]
- Brehm, L.; Günzel, H. Learning Lab “Digital Technologies”—Concept, Streams and Experiences. In Proceedings of the 4th International Conference on Higher Education Advances, Valencia, Spain, 20–22 June 2018. [Google Scholar]
- HackerRank Developer Skills Report. Insights Based on 116,648 Developers. 2021. Available online: www.hackerrank.com (accessed on 7 July 2021).
- 11 Most In-Demand Programming Languages in 2021. Available online: https://bootcamp.berkeley.edu/blog/most-in-demand-programming-languages/ (accessed on 7 July 2021).
- Law, K.M.Y.; Lee, V.C.S.; Yu, Y.T. Learning motivation in e-learning facilitated computer programming courses. Comput. Educ. 2010, 55, 218–228. [Google Scholar] [CrossRef]
- Hawi, N. Causal attributions of success and failure made by undergraduate students in an introductory-level computer programming course. Comput. Educ. 2010, 54, 1127–1136. [Google Scholar] [CrossRef]
- Govender, I. The learning context: Influence on learning to program. Comput. Educ. 2009, 53, 1218–1230. [Google Scholar] [CrossRef]
- Serrano-Cámara, L.M.; Paredes-Velasco, M.; Alcover, C.-M.; Velazquez-Iturbide, J.Á. An evaluation of students’ motivation in computer-supported collaborative learning of programming concepts. Comput. Hum. Behav. 2014, 31, 499–508. [Google Scholar] [CrossRef]
- Kintu, M.J.; Zhu, C.; Kagambe, E. Blended learning effectiveness: The relationship between student characteristics, design features and outcomes. Int. J. Educ. Technol. High. Educ. 2017, 14, 7. [Google Scholar] [CrossRef] [Green Version]
- Law, K.M.Y.; Geng, S. How innovativeness and handedness affect learning performance of engineering students? Int. J. Technol. Des. Educ. 2019, 29, 897–914. [Google Scholar] [CrossRef]
- Law, K.M.Y.; Geng, S.; Li, T. Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Comput. Educ. 2019, 136, 1–12. [Google Scholar] [CrossRef]
- Ryan, R.M.; Deci, E.L. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 2000, 55, 68–78. [Google Scholar] [CrossRef]
- Law, K.M.Y.; Breznik, K. Impacts of innovativeness and attitude on entrepreneurial intention: Among engineering and non-engineering students. Int. J. Technol. Des. Educ. 2017, 27, 683–700. [Google Scholar] [CrossRef]
- Zimmerman, B.J.; Schunk, D.H. Motivation: An essential dimension of self-regulated learning. In Motivation and Self-Regulated Learning: Theory, Research, and Applications; Lawrence Erlbaum Associates Publishers: Mahwah, NJ, USA, 2008; pp. 1–30. ISBN 978-0-8058-5898-3. [Google Scholar]
- Long, M.; Wood, C.; Littleton, K.; Passenger, T.; Sheehy, K. The Psychology of Education; Routledge: London, UK, 2010; ISBN 9780203840092. [Google Scholar]
- Vroom, V.H. Work and Motivation; Wiley: Oxford, UK, 1964. [Google Scholar]
- Harackiewicz, J.M.; Barron, K.E.; Carter, S.M.; Lehto, A.T.; Elliot, A.J. Predictors and consequences of achievement goals in the college classroom: Maintaining interest and making the grade. J. Pers. Soc. Psychol. 1997, 73, 1284–1295. [Google Scholar] [CrossRef]
- Harackiewicz, J.M.; Barron, K.E.; Elliot, A.J. Rethinking achievement goals: When are they adaptive for college students and why? Educ. Psychol. 1998, 33, 1–21. [Google Scholar] [CrossRef]
- Harackiewicz, J.M.; Barron, K.E.; Tauer, J.M.; Elliot, A.J. Predicting success in college: A longitudinal study of achievement goals and ability measures as predictors of interest and performance from freshman year through graduation. J. Educ. Psychol. 2002, 94, 562–575. [Google Scholar] [CrossRef]
- Hendry, G.D.; Lyon, P.M.; Prosser, M.; Sze, D. Conceptions of problem-based learning: The perspectives of students entering a problem-based medical program. Med. Teach. 2006, 28, 573–575. [Google Scholar] [CrossRef] [PubMed]
- Stipek, D.J. Motivation and instruction. In Handbook of Educational Psychology; Prentice Hall International: London, UK, 1996; pp. 85–113. ISBN 0-02-897089-6. [Google Scholar]
- Jenkins, T. The motivation of students of programming. In Proceedings of the 6th annual conference on Innovation and technology in computer science education (ITiCSE ’01), Canterbury, UK, 25–27 June 2001; ACM Press: New York, NY, USA, 2001; pp. 53–56. [Google Scholar]
- Skinner, B.F. Contingencies of Reinforcement: A Theoretical Analysis; Prentice Hall: Englewood Cliffs, NJ, USA, 1969; ISBN 0131717286/9780131717282.
- Chan, C.C.A.; Pearson, C.; Entrekin, L. Examining the effects of internal and external team learning on team performance. Team Perform. Manag. Int. J. 2003, 9, 174–181. [Google Scholar] [CrossRef]
- Rassuli, A.; Manzer, J.P. “Teach Us to Learn”: Multivariate Analysis of Perception of Success in Team Learning. J. Educ. Bus. 2005, 81, 21–27. [Google Scholar] [CrossRef]
- Lee, Y.; Ertmer, P.A. Examining the Effect of Small Group Discussions and Question Prompts on Vicarious Learning Outcomes. J. Res. Technol. Educ. 2006, 39, 66–80. [Google Scholar]
- Zimmerman, B.J.; Kitsantas, A. The Hidden Dimension of Personal Competence: Self-Regulated Learning and Practice. In Handbook of Competence and Motivation; Hardcover; Guilford Publications: New York, NY, USA, 2005; pp. 509–526. ISBN 1-59385-123-5. [Google Scholar]
- Locke, E.A.; Latham, G.P. A Theory of Goal Setting & Task Performance; Prentice-Hall, Inc.: Englewood Cliffs, NJ, USA, 1990; ISBN 0-13-913138-8. [Google Scholar]
- Bong, M. Academic Motivation in Self-Efficacy, Task Value, Achievement Goal Orientations, and Attributional Beliefs. J. Educ. Res. 2004, 97, 287–298. [Google Scholar] [CrossRef]
- Margolis, H.; McCabe, P.P. Self-Efficacy: A Key to Improving the Motivation of Struggling Learners. Clear. House 2004, 77, 241–249. [Google Scholar] [CrossRef]
- Barak, M. Motivating self-regulated learning in technology education. Int. J. Technol. Des. Educ. 2010, 20, 381–401. [Google Scholar] [CrossRef]
- Ng, C. What kind of students persist in science learning in the face of academic challenges? J. Res. Sci. Teach. 2021, 58, 195–224. [Google Scholar] [CrossRef]
- Zatarain Cabada, R.; Barrón Estrada, M.L.; Ríos Félix, J.M.; Alor Hernández, G. A virtual environment for learning computer coding using gamification and emotion recognition. Interact. Learn. Environ. 2020, 28, 1048–1063. [Google Scholar] [CrossRef]
- Lin, Y.G.; McKeachie, W.J.; Kim, Y.C. College student intrinsic and/or extrinsic motivation and learning. Learn. Individ. Differ. 2003, 13, 251–258. [Google Scholar] [CrossRef]
- Yin, J.; Goh, T.-T.; Yang, B.; Xiaobin, Y. Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance. J. Educ. Comput. Res. 2021, 59, 154–177. [Google Scholar] [CrossRef]
- Bosch, N.; D’Mello, S.; Mills, C. What Emotions Do Novices Experience during Their First Computer Programming Learning Session? In Proceedings of the International Conference on Artificial Intelligence in Education, Memphis, TN, USA, 9–13 July 2013; pp. 11–20. [Google Scholar]
- Bosch, N.; Chen, Y.; D’Mello, S. It’s Written on Your Face: Detecting Affective States from Facial Expressions while Learning Computer Programming. In Proceedings of the International Conference on Intelligent Tutoring Systems, Honolulu, HI, USA, 5–9 June 2014; pp. 39–44. [Google Scholar]
- Bosch, N.; D’Mello, S. The Affective Experience of Novice Computer Programmers. Int. J. Artif. Intell. Educ. 2017, 27, 181–206. [Google Scholar] [CrossRef]
- Lee, D.M.C.; Rodrigo, M.M.T.; Baker, R.S.J.D.; Sugay, J.O.; Coronel, A. Exploring the relationship between novice programmer confusion and achievement. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics; Springer: Berlin/Heidelberg, Germany, 2011; Volume 6974 LNCS, pp. 175–184. ISBN 9783642245992. [Google Scholar]
- Bosch, N.; D’Mello, S.; Baker, R.; Ocumpaugh, J.; Shute, V.; Ventura, M.; Wang, L.; Zhao, W. Automatic Detection of Learning-Centered Affective States in the Wild. In Proceedings of the 20th International Conference on Intelligent User Interfaces, Atlanta, GA, USA, 29 March–1 April 2015; ACM: New York, NY, USA, 2015; pp. 379–388. [Google Scholar]
- Bahreini, K.; Nadolski, R.; Westera, W. Towards multimodal emotion recognition in e-learning environments. Interact. Learn. Environ. 2016, 24, 590–605. [Google Scholar] [CrossRef]
- Lin, H.C.K.; Su, S.H.; Chao, C.J.; Hsieh, C.Y.; Tsai, S.C. Construction of multi-mode affective learning system: Taking Affective Design as an Example. Educ. Technol. Soc. 2016, 19, 132–147. [Google Scholar]
- D’mello, S.K.; Kory, J. A Review and Meta-Analysis of Multimodal Affect Detection Systems. ACM Comput. Surv. 2015, 47, 1–36. [Google Scholar] [CrossRef]
- Kim, Y.; Lee, H.; Provost, E.M. Deep learning for robust feature generation in audiovisual emotion recognition. In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 26–31 May 2013; pp. 3687–3691. [Google Scholar]
- Ninaus, M.; Moeller, K.; McMullen, J.; Kiili, K. Acceptance of Game-Based Learning and Intrinsic Motivation as Predictors for Learning Success and Flow Experience. Int. J. Serious Games 2017, 4, 15–30. [Google Scholar] [CrossRef] [Green Version]
- Hong, J.-C.; Hwang, M.-Y.; Tai, K.-H.; Lin, P.-H. Intrinsic motivation of Chinese learning in predicting online learning self-efficacy and flow experience relevant to students’ learning progress. Comput. Assist. Lang. Learn. 2017, 30, 552–574. [Google Scholar] [CrossRef]
- Luo, Z.; Subramaniam, G.; Steen, B. Will anxiety boost motivation? The relationship between anxiety and motivation in foreign language learning. Malays. J. ELT Res. 2020, 17, 53–72. [Google Scholar]
- Xiu, Y.; Thompson, P. Flipped University Class: A Study of Motivation and Learning. J. Inf. Technol. Educ. Res. 2020, 19, 041–063. [Google Scholar] [CrossRef]
- Young, A.M.; Wendel, P.J.; Esson, J.M.; Plank, K.M. Motivational decline and recovery in higher education STEM courses. Int. J. Sci. Educ. 2018, 40, 1016–1033. [Google Scholar] [CrossRef] [Green Version]
- Hohoff, C. Anxiety in mice and men: A comparison. J. Neural Transm. 2009, 116, 679–687. [Google Scholar] [CrossRef]
- Gordon, J.A.; Hen, R. Genetic Approaches to the Study of Anxiety. Annu. Rev. Neurosci. 2004, 27, 193–222. [Google Scholar] [CrossRef]
- Gatt, J.M.; Nemeroff, C.B.; Dobson-Stone, C.; Paul, R.H.; Bryant, R.A.; Schofield, P.R.; Gordon, E.; Kemp, A.H.; Williams, L.M. Interactions between BDNF Val66Met polymorphism and early life stress predict brain and arousal pathways to syndromal depression and anxiety. Mol. Psychiatry 2009, 14, 681–695. [Google Scholar] [CrossRef] [Green Version]
- Brown, R.J.; Skelly, N.; Chew-Graham, C.A. Online health research and health anxiety: A systematic review and conceptual integration. Clin. Psychol. Sci. Pract. 2020, 27, e12299. [Google Scholar] [CrossRef]
- Garfin, D.R.; Silver, R.C.; Holman, E.A. The novel coronavirus (COVID-2019) outbreak: Amplification of public health consequences by media exposure. Health Psychol. 2020, 39, 355–357. [Google Scholar] [CrossRef]
- Jungmann, S.M.; Witthöft, M. Health anxiety, cyberchondria, and coping in the current COVID-19 pandemic: Which factors are related to coronavirus anxiety? J. Anxiety Disord. 2020, 73, 102239. [Google Scholar] [CrossRef]
- Clark, L.A.; Watson, D. Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. J. Abnorm. Psychol. 1991, 100, 316–336. [Google Scholar] [CrossRef] [PubMed]
- Renner, K.H.; Hock, M.; Bergner-Köther, R.; Laux, L. Differentiating anxiety and depression: The State-Trait Anxiety-Depression Inventory. Cogn. Emot. 2018, 32, 1409–1423. [Google Scholar] [CrossRef]
- Iversen, A.; Wessely, S. Chronic fatigue and depression. Curr. Opin. Psychiatry 2003, 16, 17–21. [Google Scholar] [CrossRef]
- Payne, S.C.; Youngcourt, S.S.; Beaubien, J.M. A meta-analytic examination of the goal orientation nomological net. J. Appl. Psychol. 2007, 92, 128–150. [Google Scholar] [CrossRef] [PubMed]
- Vedel, A. The Big Five and tertiary academic performance: A systematic review and meta-analysis. Pers. Individ. Dif. 2014, 71, 66–76. [Google Scholar] [CrossRef] [Green Version]
- Ruiz, J.G.; Mintzer, M.J.; Leipzig, R.M. The Impact of E-Learning in Medical Education. Acad. Med. 2006, 81, 207–212. [Google Scholar] [CrossRef]
- Sun, P.-C.; Tsai, R.J.; Finger, G.; Chen, Y.-Y.; Yeh, D. What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Comput. Educ. 2008, 50, 1183–1202. [Google Scholar] [CrossRef]
- Lee, M.C. Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Comput. Educ. 2010, 54, 506–516. [Google Scholar] [CrossRef]
- Lawrence, S.A.; Garcia, J.; Stewart, C.; Rodriguez, C. The mental and behavioral health impact of COVID-19 stay at home orders on social work students. Soc. Work Educ. 2021, 1, 1–15. [Google Scholar]
- Escudero-Castillo, I.; Mato-Díaz, F.J.; Rodriguez-Alvarez, A. Furloughs, Teleworking and Other Work Situations during the COVID-19 Lockdown: Impact on Mental Well-Being. Int. J. Environ. Res. Public Health 2021, 18, 2898. [Google Scholar] [CrossRef]
- Fawaz, M.; Samaha, A. e-learning: Depression, anxiety, and stress symptomatology among Lebanese university students during COVID-19 quarantine. Nurs. Forum 2021, 56, 52–57. [Google Scholar] [CrossRef]
- Othman, N.; Ahmad, F.; El Morr, C.; Ritvo, P. Perceived impact of contextual determinants on depression, anxiety and stress: A survey with university students. Int. J. Ment. Health Syst. 2019, 13, 17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nurunnabi, M.; Hossain, S.F.A.H.; Chinna, K.; Sundarasen, S.; Khoshaim, H.B.; Kamaludin, K.; Baloch, G.M.; Sukayt, A.; Shan, X. Coping strategies of students for anxiety during the COVID-19 pandemic in China: A cross-sectional study. F1000Research 2020, 9, 1115. [Google Scholar] [CrossRef]
- Jojoa, M.; Lazaro, E.; Garcia-Zapirain, B.; Gonzalez, M.J.; Urizar, E. The Impact of COVID 19 on University Staff and Students from Iberoamerica: Online Learning and Teaching Experience. Int. J. Environ. Res. Public Health 2021, 18, 5820. [Google Scholar] [CrossRef]
- Brooks, S.K.; Webster, R.K.; Smith, L.E.; Woodland, L.; Wessely, S.; Greenberg, N.; Rubin, G.J. The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Lancet 2020, 395, 912–920. [Google Scholar] [CrossRef] [Green Version]
- Odriozola-González, P.; Planchuelo-Gómez, Á.; Irurtia, M.J.; de Luis-García, R. Psychological effects of the COVID-19 outbreak and lockdown among students and workers of a Spanish university. Psychiatry Res. 2020, 290, 113108. [Google Scholar] [CrossRef] [PubMed]
- Cao, W.; Fang, Z.; Hou, G.; Han, M.; Xu, X.; Dong, J.; Zheng, J. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Res. 2020, 287, 112934. [Google Scholar] [CrossRef] [PubMed]
- Aslan, I.; Ochnik, D.; Çınar, O. Exploring Perceived Stress among Students in Turkey during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2020, 17, 8961. [Google Scholar] [CrossRef] [PubMed]
- MacWhinnie, S.G.B.; Mitchell, C. English classroom reforms in Japan: A study of Japanese university EFL student anxiety and motivation. Asian-Pac. J. Second Foreign Lang. Educ. 2017, 2, 7. [Google Scholar] [CrossRef]
- Al-Tammemi, A.B.; Akour, A.; Alfalah, L. Is It Just About Physical Health? An Online Cross-Sectional Study Exploring the Psychological Distress Among University Students in Jordan in the Midst of COVID-19 Pandemic. Front. Psychol. 2020, 11, 562213. [Google Scholar] [CrossRef]
- Friedman, N.P.; Miyake, A.; Young, S.E.; DeFries, J.C.; Corley, R.P.; Hewitt, J.K. Individual differences in executive functions are almost entirely genetic in origin. J. Exp. Psychol. Gen. 2008, 137, 201–225. [Google Scholar] [CrossRef] [PubMed]
- Hong, J.-C.; Hwang, M.-Y.; Chang, H.-W.; Tai, K.-H.; Kuo, Y.-C.; Tsai, Y.-H. Internet cognitive failure and fatigue relevant to learners’ self-regulation and learning progress in English vocabulary with a calibration scheme. J. Comput. Assist. Learn. 2015, 31, 450–461. [Google Scholar] [CrossRef]
- Christodoulou, C. The Assessment and Measurement of Fatigue. In Fatigue as A Window to the Brain; MIT Press: Cambridge, MA, USA, 2005; pp. 19–35. ISBN 0-262-04227-4. [Google Scholar]
- DeLuca, J. Fatigue, Cognition, and Mental Effort. In Fatigue as A Window to the Brain; Issues in clinical and cognitive neuropsychology; MIT Press: Cambridge, MA, USA, 2005; pp. 37–57. ISBN 0-262-04227-4. [Google Scholar]
- Kanfer, R. Determinants and consequences of subjective cognitive fatigue. In Cognitive Fatigue: Multidisciplinary Perspectives on Current Research and Future Applications; American Psychological Association: Washington, WA, USA, 2011; pp. 189–207. [Google Scholar]
- Ding, L.; Velicer, W.F.; Harlow, L.L. Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Struct. Equ. Model. Multidiscip. J. 1995, 2, 119–143. [Google Scholar] [CrossRef]
- Law, K.M.Y.; Sandnes, F.E.; Jian, H.L.; Huang, Y.P. A comparative study of learning motivation among engineering students in south east asia and beyond. Int. J. Eng. Educ. 2009, 25, 144. [Google Scholar]
- Kroenke, K.; Spitzer, R.L.; Williams, J.B.W. The PHQ-9. J. Gen. Intern. Med. 2001, 16, 606–613. [Google Scholar] [CrossRef]
- Spitzer, R.L.; Kroenke, K.; Williams, J.B.W.; Löwe, B. A Brief Measure for Assessing Generalized Anxiety Disorder. Arch. Intern. Med. 2006, 166, 1092. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Smets, E.M.A.; Garssen, B.; Bonke, B.; De Haes, J.C.J.M. The multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J. Psychosom. Res. 1995, 39, 315–325. [Google Scholar] [CrossRef] [Green Version]
- Kocalevent, R.-D.; Hinz, A.; Brähler, E. Standardization of the depression screener Patient Health Questionnaire (PHQ-9) in the general population. Gen. Hosp. Psychiatry 2013, 35, 551–555. [Google Scholar] [CrossRef]
- Löwe, B.; Decker, O.; Müller, S.; Brähler, E.; Schellberg, D.; Herzog, W.; Herzberg, P.Y. Validation and Standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the General Population. Med. Care 2008, 46, 266–274. [Google Scholar] [CrossRef]
- Schwarz, R.; Krauss, O.; Hinz, A. Fatigue in the General Population. Oncol. Res. Treat. 2003, 26, 140–144. [Google Scholar] [CrossRef]
- Kline, R.B. Principles and Practice of Structural Equation Modeling, 4th ed.; Methodology in the Social Sciences; Guilford Press: New York, NY, USA, 2016; ISBN 978-1-4625-2334-4. [Google Scholar]
- Bryne, B.M. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming; Routledge: London, UK, 2013. [Google Scholar]
- Murtaugh, P.A. In defense of P values. Ecology 2014, 95, 611–617. [Google Scholar] [CrossRef] [Green Version]
- Tang, Y.M.; Chen, P.C.; Law, K.M.Y.; Wu, C.H.; Lau, Y.; Guan, J.; He, D.; Ho, G.T.S. Comparative analysis of Student’s live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Comput. Educ. 2021, 168, 104211. [Google Scholar] [CrossRef] [PubMed]
- Frey, A.-L.; McCabe, C. Impaired social learning predicts reduced real-life motivation in individuals with depression: A computational fMRI study. J. Affect. Disord. 2020, 263, 698–706. [Google Scholar] [CrossRef] [PubMed]
- McRae, K.; Gross, J.J. Emotion regulation. Emotion 2020, 20, 1–9. [Google Scholar] [CrossRef] [PubMed]
- Brackney, B.E.; Karabenick, S.A. Psychopathology and academic performance: The role of motivation and learning strategies. J. Couns. Psychol. 1995, 42, 456–465. [Google Scholar] [CrossRef]
- Garvik, M.; Idsoe, T.; Bru, E. Motivation and Social Relations in School Following a CBT Course for Adolescents With Depressive Symptoms: An Effectiveness Study. Scand. J. Educ. Res. 2016, 60, 219–239. [Google Scholar] [CrossRef] [Green Version]
- Au, R.C.P.; Watkins, D.; Hattie, J.; Alexander, P. Reformulating the depression model of learned hopelessness for academic outcomes. Educ. Res. Rev. 2009, 4, 103–117. [Google Scholar] [CrossRef]
- Roeser, R.W.; Strobel, K.R.; Quihuis, G. Studying Early Adolescents’ Academic Motivation, Social-Emotional Functioning, and Engagement in Learning: Variable- and Person-Centered Approaches. Anxiety Stress Coping 2002, 15, 345–368. [Google Scholar] [CrossRef]
- Essau, C.A.; Leung, P.W.L.; Conradt, J.; Cheng, H.; Wong, T. Anxiety symptoms in Chinese and German adolescents: Their relationship with early learning experiences, perfectionism, and learning motivation. Depress. Anxiety 2008, 25, 801–810. [Google Scholar] [CrossRef]
- Zeng, L.; Chen, D.; Xiong, K.; Pang, A.; Huang, J.; Zeng, L. Medical University Students’ Personality and Learning Performance: Learning Burnout as a Mediator. In Proceedings of the 2015 7th International Conference on Information Technology in Medicine and Education, Huangshan, China, 13–15 November 2015; pp. 492–495. [Google Scholar]
- Lin, S.-H.; Huang, Y.-C. Investigating the relationships between loneliness and learning burnout. Act. Learn. High. Educ. 2012, 13, 231–243. [Google Scholar] [CrossRef]
- van Vendeloo, S.N.; Prins, D.J.; Verheyen, C.C.P.M.; Prins, J.T.; van den Heijkant, F.; van der Heijden, F.M.M.A.; Brand, P.L.P. The learning environment and resident burnout: A national study. Perspect. Med. Educ. 2018, 7, 120–125. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.-Y.; Shu, T.; Xiang, M.; Feng, Z.-C. Learning Burnout: Evaluating the Role of Social Support in Medical Students. Front. Psychol. 2021, 12, 625506. [Google Scholar] [CrossRef]
- Lenaert, B.; Boddez, Y.; Vlaeyen, J.W.S.; van Heugten, C.M. Learning to feel tired: A learning trajectory towards chronic fatigue. Behav. Res. Ther. 2018, 100, 54–66. [Google Scholar] [CrossRef] [PubMed]
- Asadayoobi, N.; Jaber, M.Y.; Taghipour, S. A new learning curve with fatigue-dependent learning rate. Appl. Math. Model. 2021, 93, 644–656. [Google Scholar] [CrossRef]
- Forsythe, A.; Jellicoe, M. Predicting gainful learning in Higher Education: A goal-orientation approach. High. Educ. Pedagog. 2018, 3, 103–117. [Google Scholar] [CrossRef] [Green Version]
- Mills, J.S.; Blankstein, K.R. Perfectionism, intrinsic vs extrinsic motivation, and motivated strategies for learning: A multidimensional analysis of university students. Pers. Individ. Dif. 2000, 29, 1191–1204. [Google Scholar] [CrossRef]
- Nguyen, T.D.; Shultz, C.J.; Westbrook, M.D. Psychological Hardiness in Learning and Quality of College Life of Business Students: Evidence from Vietnam. J. Happiness Stud. 2012, 13, 1091–1103. [Google Scholar] [CrossRef]
- Li, W.; Lee, A.M.; Solmon, M. Effects of Dispositional Ability Conceptions, Manipulated Learning Environments, and Intrinsic Motivation on Persistence and Performance. Res. Q. Exerc. Sport 2008, 79, 51–61. [Google Scholar]
- Karlen, Y.; Suter, F.; Hirt, C.; Maag Merki, K. The role of implicit theories in students’ grit, achievement goals, intrinsic and extrinsic motivation, and achievement in the context of a long-term challenging task. Learn. Individ. Differ. 2019, 74, 101757. [Google Scholar] [CrossRef]
- Rigolizzo, M.; Zhu, Z. The ebb and flow of learning motivation: The differentiated impact of the implicit theory of intelligence on learning behaviors. Hum. Resour. Dev. Q. 2021, 1, hrdq.21425. [Google Scholar]
- Reis, S.; Coelho, F.; Coelho, L. Success Factors in Students’ Motivation with Project Based Learning: From Theory to Reality. Int. J. Online Biomed. Eng. 2020, 16, 4. [Google Scholar] [CrossRef]
- Truzoli, R.; Viganò, C.; Galmozzi, P.G.; Reed, P. Problematic internet use and study motivation in higher education. J. Comput. Assist. Learn. 2020, 36, 480–486. [Google Scholar] [CrossRef]
- Reed, P.; Reay, E. Relationship between levels of problematic Internet usage and motivation to study in university students. High. Educ. 2015, 70, 711–723. [Google Scholar] [CrossRef] [Green Version]
- Santamaría-Vázquez, M.; Del Líbano, M.; Martínez-Lezaun, I.; Ortiz-Huerta, J.H. Self-Regulation of Motivation and Confinement by COVID-19: A Study in Spanish University Students. Sustainability 2021, 13, 5435. [Google Scholar] [CrossRef]
- Moore, K.P.; Richards, A.S. The Effects of Instructor Credibility, Grade Incentives, and Framing of a Technology Policy on Students’ Intent to Comply and Motivation to Learn. Commun. Stud. 2019, 70, 394–411. [Google Scholar] [CrossRef]
- Murty, V.P.; LaBar, K.S.; Hamilton, D.A.; Adcock, R.A. Is all motivation good for learning? Dissociable influences of approach and avoidance motivation in declarative memory. Learn. Mem. 2011, 18, 712–717. [Google Scholar] [CrossRef] [Green Version]
- Luria, E.; Shalom, M.; Levy, D.A. Cognitive Neuroscience Perspectives on Motivation and Learning: Revisiting Self-Determination Theory. Mind Brain Educ. 2021, 15, 5–17. [Google Scholar] [CrossRef]
- Jayalath, J.; Esichaikul, V. Gamification to Enhance Motivation and Engagement in Blended eLearning for Technical and Vocational Education and Training. Technol. Knowl. Learn. 2020, 1–28. [Google Scholar] [CrossRef]
- Borovay, L.A.; Shore, B.M.; Caccese, C.; Yang, E.; Hua, O. Flow, Achievement Level, and Inquiry-Based Learning. J. Adv. Acad. 2019, 30, 74–106. [Google Scholar] [CrossRef]
Scales and Subscales | Cronbach Alpha |
---|---|
GAD-7 | 0.915 |
PHQ-9 | 0.889 |
MFI-20 | 0.933 |
General Fatigue | 0.823 |
Physical Fatigue | 0.869 |
Reduced Activity | 0.784 |
Reduced Motivation | 0.671 |
Mental Fatigue | 0.854 |
Learning Motivating Factors Questionnaire (LMF) | 0.868 |
Individual Attitude and Expectation | 0.811 |
Challenging Goals | 0.866 |
Clear Direction | 0.698 |
Reward and Recognition | 0.743 |
Punishment | 0.778 |
Social pressure and competition | 0.824 |
PHQ-9 | Frequency | Percent | 95% CI | p |
---|---|---|---|---|
Minimal depression (0–4) | 108 | 24.3 | [2.06–2.61] | <0.001 |
Mild depression (5–9) | 130 | 29.3 | [6.71–7.20] | <0.001 |
Moderate depression (10–14) | 74 | 16.7 | [11.62–12.33] | <0.001 |
Moderately severe depression (15–19) | 50 | 11.3 | [16.31–17.05] | <0.001 |
Severe depression (20–27) | 32 | 7.2 | [21.33–22.86] | <0.001 |
GAD-7 | Frequency | Percent | 95% CI | p |
---|---|---|---|---|
Minimal anxiety (0–4) | 161 | 36.3 | [1.98–2.42] | <0.001 |
Mild anxiety (5–9) | 151 | 34 | [6.41–6.85] | <0.001 |
Moderate anxiety (10–14) | 73 | 16.4 | [11.43–12.11] | <0.001 |
Severe anxiety (15–21) | 53 | 11.9 | [17.36–18.46] | <0.001 |
MFI-20: General Fatigue | Frequency | Percent | 95% CI | p |
---|---|---|---|---|
Minimal fatigue (4–7) | 53 | 11.9 | [5.56–6.21] | <0.001 |
Mild fatigue (8–11) | 133 | 30 | [9.49–9.87] | <0.001 |
Moderate fatigue (12–15) | 168 | 37.8 | [13.31–13.65] | <0.001 |
Severe fatigue (16–20) | 90 | 20.3 | [16.99–17.59] | <0.001 |
MFI-20 Variables | M | SD | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|---|
Physical fatigue | 2.813 | 1.030 | - | |||
Reduced activity | 2.988 | 0.905 | 0.630 *** | - | ||
Reduced motivation | 2.512 | 0.790 | 0.569 *** | 0.729 *** | - | |
Mental fatigue | 2.846 | 0.911 | 0.416 *** | 0.647 *** | 0.631 *** | - |
General fatigue | 3.052 | 0.934 | 0.748 *** | 0.661 *** | 0.615 *** | 0.545 *** |
Learning Motivation Variables | M | SD | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|---|---|
Individual attitude and expectation | 4.71 | 0.86 | - | ||||
Challenging goals | 4.33 | 1.08 | 0.407 *** | - | |||
Clear direction | 4.97 | 0.78 | 0.648 *** | 0.449 *** | - | ||
Reward and recognition | 4.85 | 0.90 | 0.546 *** | 0.118 * | 0.504 *** | - | |
Punishment | 3.46 | 1.33 | 0.230 *** | 0.158 ** | 0.257 *** | 0.161 ** | - |
Social pressure and competition | 3.46 | 1.21 | 0.295 *** | 0.284 *** | 0.199 *** | 0.230 *** | 0.422 *** |
Scales | M | SD | 1 |
---|---|---|---|
PHQ-9 | 9.094 | 6.240 | - |
GAD-7 | 7.221 | 5.378 | 0.499 *** |
Learning Motivating Factors | Depression Categories | N | Mean | Standard Deviation | Standard Error | 95% Confidence Interval for Mean | |
---|---|---|---|---|---|---|---|
Lower B | Upper B | ||||||
Individual attitude and expectation | Minimal depression | 108 | 4.859 | 0.783 | 0.075 | 4.709 | 5.008 |
Mild depression | 130 | 4.764 | 0.818 | 0.071 | 4.621 | 4.905 | |
Moderate depression | 73 | 4.678 | 0.848 | 0.099 | 4.480 | 4.876 | |
Moderately severe depression | 49 | 4.403 | 0.904 | 0.129 | 4.143 | 4.662 | |
Severe depression | 32 | 4.445 | 1.114 | 0.197 | 4.043 | 4.846 | |
Challenging goals | Minimal depression | 108 | 4.833 | 0.808 | 0.077 | 4.679 | 4.988 |
Mild depression | 130 | 4.354 | 1.091 | 0.095 | 4.164 | 4.543 | |
Moderate depression | 73 | 4.073 | 1.026 | 0.120 | 3.833 | 4.312 | |
Moderately severe depression | 49 | 3.708 | 1.079 | 0.154 | 3.398 | 4.018 | |
Severe depression | 32 | 3.844 | 1.173 | 0.207 | 3.420 | 4.267 | |
Clear direction | Minimal depression | 108 | 5.172 | 0.737 | 0.070 | 5.032 | 5.313 |
Mild depression | 130 | 5.015 | 0.719 | 0.063 | 4.890 | 5.140 | |
Moderate depression | 73 | 4.995 | 0.748 | 0.087 | 4.820 | 5.170 | |
Moderately severe depression | 49 | 4.612 | 0.681 | 0.097 | 4.417 | 4.808 | |
Severe depression | 32 | 4.583 | 1.057 | 0.187 | 4.202 | 4.965 | |
Reward and recognition | Minimal depression | 108 | 4.811 | 0.971 | 0.093 | 4.626 | 4.997 |
Mild depression | 130 | 4.917 | 0.809 | 0.071 | 4.778 | 5.058 | |
Moderate depression | 73 | 4.954 | 0.863 | 0.101 | 4.753 | 5.156 | |
Moderately severe depression | 49 | 4.776 | 0.989 | 0.141 | 4.491 | 5.060 | |
Severe depression | 32 | 4.646 | 0.939 | 0.166 | 4.307 | 4.984 | |
Punishment | Minimal depression | 108 | 3.435 | 1.354 | 0.130 | 3.177 | 3.693 |
Mild depression | 130 | 3.527 | 1.203 | 0.106 | 3.318 | 3.736 | |
Moderate depression | 73 | 3.596 | 1.384 | 0.162 | 3.273 | 3.919 | |
Moderately severe depression | 49 | 2.980 | 1.342 | 0.192 | 2.594 | 3.365 | |
Severe depression | 32 | 3.516 | 1.511 | 0.267 | 2.971 | 4.060 | |
Social pressure and competition | Minimal depression | 108 | 3.495 | 1.170 | 0.113 | 3.272 | 3.719 |
Mild depression | 130 | 3.604 | 1.176 | 0.103 | 3.400 | 3.808 | |
Moderate depression | 73 | 3.500 | 1.187 | 0.139 | 3.223 | 3.777 | |
Moderately severe depression | 49 | 2.975 | 1.198 | 0.171 | 2.630 | 3.319 | |
Severe depression | 32 | 3.297 | 1.418 | 0.251 | 2.786 | 3.808 |
Learning Motivating Factors | Anxiety Categories | N | Mean | Standard Deviation | Standard Error | 95% Confidence Interval for Mean | |
---|---|---|---|---|---|---|---|
Lower B | Upper B | ||||||
Individual attitude and expectation | Minimal anxiety | 148 | 4.797 | 0.832 | 0.068 | 4.662 | 4.932 |
Mild anxiety | 137 | 4.725 | 0.885 | 0.076 | 4.575 | 4.874 | |
Moderate anxiety | 71 | 4.585 | 0.828 | 0.982 | 4.389 | 4.781 | |
Severe anxiety | 49 | 4.612 | 0.920 | 0.131 | 4.348 | 4.877 | |
Challenging goals | Minimal anxiety | 148 | 4.604 | 1.020 | 0.084 | 4.438 | 4.516 |
Mild anxiety | 137 | 4.329 | 1.112 | 0.095 | 4.140 | 4.516 | |
Moderate anxiety | 71 | 4.155 | 0.946 | 0.112 | 3.931 | 4.378 | |
Severe anxiety | 49 | 3.782 | 1.154 | 0.165 | 3.450 | 4.114 | |
Clear direction | Minimal anxiety | 148 | 5.045 | 0.763 | 0.063 | 4.921 | 5.169 |
Mild anxiety | 137 | 5.059 | 0.759 | 0.065 | 4.930 | 5.187 | |
Moderate anxiety | 71 | 4.826 | 0.756 | 0.090 | 4.647 | 5.005 | |
Severe anxiety | 49 | 4.728 | 0.889 | 0.127 | 4.472 | 4.983 | |
Reward and recognition | Minimal anxiety | 148 | 4.746 | 0.925 | 0.076 | 4.595 | 4.896 |
Mild anxiety | 137 | 4.966 | 0.883 | 0.075 | 4.817 | 5.115 | |
Moderate anxiety | 71 | 4.859 | 0.861 | 0.102 | 4.655 | 5.063 | |
Severe anxiety | 49 | 4.803 | 0.902 | 0.133 | 4.536 | 4.935 | |
Punishment | Minimal anxiety | 148 | 3.449 | 1.326 | 0.109 | 3.234 | 3.665 |
Mild anxiety | 137 | 3.533 | 1.320 | 0.113 | 3.310 | 3.756 | |
Moderate anxiety | 71 | 3.521 | 1.291 | 0.153 | 3.216 | 3.827 | |
Severe anxiety | 49 | 3.214 | 1.331 | 0.066 | 3.332 | 3.592 | |
Social pressure and competition | Minimal anxiety | 148 | 3.525 | 1.127 | 0.093 | 3.342 | 3.708 |
Mild anxiety | 137 | 3.564 | 1.327 | 0.113 | 3.340 | 3.788 | |
Moderate anxiety | 71 | 3.450 | 1.090 | 0.129 | 3.192 | 3.709 | |
Severe anxiety | 49 | 3.010 | 1.187 | 0.170 | 2.670 | 3.581 |
Learning Motivating Factors | Fatigue Categories | N | Mean | Standard Deviation | Standard Error | 95% Confidence Interval for Mean | |
---|---|---|---|---|---|---|---|
Lower B | Upper B | ||||||
Individual attitude and expectation | Minimal fatigue | 48 | 4.781 | 0.844 | 0.122 | 4.536 | 5.026 |
Mild fatigue | 122 | 4.730 | 0.848 | 0.077 | 4.578 | 4.881 | |
Moderate fatigue | 156 | 4.801 | 0.831 | 0.067 | 4.670 | 4.933 | |
Severe fatigue | 80 | 4.481 | 0.918 | 0.103 | 4.277 | 4.686 | |
Challenging goals | Minimal fatigue | 48 | 4.778 | 1.112 | 0.160 | 4.455 | 5.100 |
Mild fatigue | 122 | 4.459 | 1.058 | 0.096 | 4.269 | 4.649 | |
Moderate fatigue | 156 | 4.306 | 1.023 | 0.082 | 4.143 | 4.467 | |
Severe fatigue | 80 | 3.921 | 1.096 | 0.123 | 3.677 | 4.416 | |
Clear direction | Minimal fatigue | 48 | 5.076 | 0.951 | 0.137 | 4.800 | 5.352 |
Mild fatigue | 122 | 5.071 | 0.699 | 0.063 | 4.946 | 5.196 | |
Moderate fatigue | 156 | 4.915 | 0.804 | 0.644 | 4.787 | 5.042 | |
Severe fatigue | 80 | 4.879 | 0.741 | 0.829 | 4.714 | 5.044 | |
Reward and recognition | Minimal fatigue | 48 | 4.542 | 1.104 | 0.159 | 4.221 | 4.862 |
Mild fatigue | 122 | 4.962 | 0.821 | 0.744 | 4.815 | 5.109 | |
Moderate fatigue | 156 | 4.853 | 0.899 | 0.072 | 4.7104 | 4.995 | |
Severe fatigue | 80 | 4.858 | 0.873 | 0.098 | 4.664 | 5.053 | |
Punishment | Minimal fatigue | 48 | 3.229 | 1.313 | 0.189 | 2.848 | 3.610 |
Mild fatigue | 122 | 3.610 | 1.373 | 0.124 | 3.365 | 3.857 | |
Moderate fatigue | 156 | 3.490 | 1.249 | 0.100 | 3.293 | 3.688 | |
Severe fatigue | 80 | 3.313 | 1.415 | 0.158 | 2.998 | 3.627 | |
Social pressure and competition | Minimal fatigue | 48 | 3.172 | 1.244 | 0.180 | 2.811 | 3.533 |
Mild fatigue | 122 | 3.687 | 1.255 | 0.114 | 3.418 | 3.911 | |
Moderate fatigue | 156 | 3.585 | 1.057 | 0.085 | 3.418 | 3.752 | |
Severe fatigue | 80 | 3.053 | 1.270 | 0.142 | 2.770 | 3.336 |
Model | Non-Standardized Coefficients | Standardized Coefficients | t | Significance | |
---|---|---|---|---|---|
B | Standard Error | Beta | |||
A. Respondents do not participate in e-learning-based computer programming courses | |||||
(Constant) | 12.244 | 2.655 | 4.612 | 0.000 | |
Individual attitude and expectation | −0.065 | 0.642 | −0.010 | −0.102 | 0.919 |
Challenging goals | −0.592 | 0.401 | −0.126 | −1.477 | 0.141 |
Clear direction | −0.581 | 0.661 | −0.085 | −0.879 | 0.381 |
Reward and recognition | 0.033 | 0.543 | 0.005 | 0.060 | 0.952 |
Punishment | 0.037 | 0.301 | 0.010 | 0.124 | 0.901 |
Social pressure and competition | 0.141 | 0.347 | 0.033 | 0.405 | 0.686 |
R = 0.181; R Square = 0.033; Adjusted R Square = 0.005; Standard Error of the Estimate = 5.24862; F (6, 212) = 1.192, p = 0.312 | |||||
B. Respondents participate in e-learning-based computer programming courses | |||||
(Constant) | 15.287 | 2.747 | 5.565 | 0.000 | |
Individual attitude and expectation | 0.245 | 0.608 | 0.040 | 0.404 | 0.687 |
Challenging goals | −1.692 | 0.417 | −0.318 | −4.061 | 0.000 |
Clear direction | −0.935 | 0.702 | −0.134 | −1.331 | 0.185 |
Reward and recognition | 0.783 | 0.499 | 0.131 | 1.570 | 0.118 |
Punishment | 0.185 | 0.330 | 0.043 | 0.561 | 0.576 |
Social pressure and competition | −0.431 | 0.364 | −0.091 | −1.183 | 0.239 |
R = 0.395; R Square = 0.156; Adjusted R Square = 0.128; Standard Error of the Estimate = 5.16559; F (6, 179) = 5.508, p < 0.001 |
Model | Non-Standardized Coefficients | Standardized Coefficients | t | Significance | |
---|---|---|---|---|---|
B | Standard Error | Beta | |||
A. Respondents do not participate in e-learning-based computer programming courses | |||||
(Constant) | 18.068 | 3.077 | 5.872 | 0.000 | |
Individual attitude and expectation | −0.192 | 0.747 | −0.026 | −0.257 | 0.797 |
Challenging goals | −1.133 | 0.459 | −0.211 | −2.469 | 0.014 |
Clear direction | −0.464 | 0.758 | −0.059 | −0.612 | 0.541 |
Reward and recognition | −0.271 | 0.641 | −0.038 | −0.423 | 0.673 |
Punishment | 0.175 | 0.346 | 0.040 | 0.507 | 0.613 |
Social pressure and competition | −0.014 | 0.394 | -0.003 | −0.036 | 0.972 |
R = 0.272; R Square = 0.074; Adjusted R Square = 0.046; Stadard Error of the Estimate = 5.86205; F (6, 199) = 2.656, p = 0.017 | |||||
B. Respondents participate in e-learning-based computer programming courses | |||||
(Constant) | 24.130 | 3.065 | 7.873 | 0.000 | |
Individual attitude and expectation | 0.370 | 0.677 | 0.052 | 0.546 | 0.586 |
Challenging goals | −2.233 | 0.464 | −0.357 | −4.809 | 0.000 |
Clear direction | −2.197 | 0.783 | −0.268 | −2.806 | 0.006 |
Reward and recognition | 0.874 | 0.554 | 0.125 | 1.578 | 0.116 |
Punishment | 0.335 | 0.368 | 0.066 | 0.911 | 0.363 |
Social pressure and competition | −0.478 | 0.407 | −0.085 | −1.176 | 0.241 |
R = 0.493; R Square = 0.243; Adjusted R Square = 0.218; Standard Error of the Estimate = 5.75620; F (6, 179) = 9.584, p < 0.001 |
Model | Non-Standardized Coefficients | Standardized Coefficients | t | Significance | |
---|---|---|---|---|---|
B | Standard Error | Beta | |||
A. Respondents do not participate in e-learning-based computer programming courses | |||||
(Constant) | 2.072 | 0.466 | 4.443 | 0.000 | |
Individual attitude and expectation | 0.203 | 0.102 | 0.184 | 1.982 | 0.049 |
Challenging goals | −0.034 | 0.064 | −0.043 | −0.539 | 0.590 |
Clear direction | −0.028 | 0.104 | −0.024 | −0.272 | 0.786 |
Reward and recognition | −0.068 | 0.088 | −0.065 | −0.777 | 0.438 |
Punishment | −0.030 | 0.047 | −0.045 | −0.623 | 0.534 |
Social pressure and competition | 0.011 | 0.054 | 0.015 | 0.200 | 0.842 |
Anxiety (GAD-7) | 0.061 | 0.011 | 0.360 | 5.557 | 0.000 |
Depression (PHQ-9) | 0.033 | 0.010 | 0.219 | 3.296 | 0.001 |
R = 0.472; R Square = 0.223; Adjusted R Square = 0.191; Standard Error of the Estimate = 0.80231; F (8, 197) = 7.058, p < 0.001 | |||||
B. Respondents participate in e-learning-based computer programming courses | |||||
(Constant) | 1.712 | 0.394 | 4.346 | 0.000 | |
Individual attitude and expectation | −0.262 | 0.075 | −0.246 | −3.514 | 0.001 |
Challenging goals | −0.016 | 0.054 | −0.017 | −0.298 | 0.766 |
Clear direction | 0.209 | 0.088 | 0.172 | 2.369 | 0.019 |
Reward and recognition | 0.121 | 0.062 | 0.116 | 1.957 | 0.052 |
Punishment | 0.008 | 0.041 | 0.011 | 0.199 | 0.842 |
Social pressure and competition | −0.017 | 0.045 | −0.020 | −0.374 | 0.709 |
Anxiety (GAD-7) | 0.039 | 0.013 | 0.222 | 2.907 | 0.004 |
Depression (PHQ-9) | 0.082 | 0.012 | 0.551 | 6.812 | 0.000 |
R = 0.768; R Square = 0.589; Adjusted R Square = 0.571; Standard Error of the Estimate = 0.63359; F (8, 176) = 31.554, p < 0.001 |
Model | Non-Standardized Coefficients | Standardized Coefficients | t | Significance | |
---|---|---|---|---|---|
B | Standard Error | Beta | |||
A. Respondents do not participate in e-learning-based computer programming courses | |||||
(Constant) | 3.247 | 0.478 | 6.787 | 0.000 | |
Individual attitude and expectation | 0.079 | 0.105 | 0.072 | 0.754 | 0.452 |
Challenging goals | −0.123 | 0.066 | −0.154 | −1.871 | 0.063 |
Clear direction | −0.034 | 0.107 | −0.029 | −0.320 | 0.749 |
Reward and recognition | −0.127 | 0.090 | −0.121 | −1.413 | 0.159 |
Punishment | 0.005 | 0.049 | 0.007 | 0.098 | 0.922 |
Social pressure and competition | 0.000 | 0.055 | 0.001 | 0.009 | 0.993 |
Anxiety (GAD-7) | 0.041 | 0.011 | 0.243 | 3.645 | 0.000 |
Depression (PHQ-9) | 0.026 | 0.010 | 0.177 | 2.580 | 0.011 |
R = 0.419; R Square = 0.175; Adjusted R Square = 0.142; Standard Error of the Estimate = 0.82302; F (8, 197) = 5.229, p < 0.001 | |||||
B. Respondents participate in e-learning-based computer programming courses | |||||
(Constant) | 2.451 | 0.416 | 5.893 | 0.000 | |
Individual attitude and expectation | −0.131 | 0.079 | −0.123 | −1.657 | 0.099 |
Challenging goals | −0.252 | 0.057 | −0.274 | −4.387 | 0.000 |
Clear direction | 0.148 | 0.093 | 0.122 | 1.584 | 0.115 |
Reward and recognition | 0.122 | 0.065 | 0.118 | 1.876 | 0.062 |
Punishment | −0.095 | 0.043 | −0.127 | −2.217 | 0.028 |
Social pressure and competition | 0.092 | 0.047 | 0.111 | 1.933 | 0.055 |
Anxiety (GAD-7) | 0.017 | 0.014 | 0.100 | 1.229 | 0.221 |
Depression (PHQ-9) | 0.073 | 0.013 | 0.490 | 5.695 | 0.000 |
R = 0.732; R Square = 0.535; Adjusted R Square = 0.514; Standard Error of the Estimate = 0.66896; F (8, 176) = 25.338, p < 0.001 |
Factors | Variables | B | SE | CR | β | |
---|---|---|---|---|---|---|
Punishment | -> | LMF Item 14 | 1.000 | 0.929 | ||
Punishment | -> | LMF Item 15 | 0.778 | 0.162 | 4.804 | 0.716 |
Social pressure and competition | -> | LMF Item 16 | 1.000 | 0.625 | ||
Social pressure and competition | -> | LMF Item 17 | 0.877 | 0.126 | 6.978 | 0.636 |
Social pressure and competition | -> | LMF Item 18 | 1.056 | 0.134 | 7.867 | 0.756 |
Social pressure and competition | -> | LMF Item 19 | 1.246 | 0.152 | 8.183 | 0.835 |
Anxiety | -> | GAD-7 Item 1 | 1.000 | 0.866 | ||
Anxiety | -> | GAD-7 Item 2 | 1.027 | 0.063 | 16.239 | 0.874 |
Anxiety | -> | GAD-7 Item 3 | 1.074 | 0.067 | 16.142 | 0.872 |
Anxiety | -> | GAD-7 Item 4 | 0.870 | 0.068 | 12.832 | 0.764 |
Anxiety | -> | GAD-7 Item 5 | 0.701 | 0.062 | 11.328 | 0.704 |
Anxiety | -> | GAD-7 Item 6 | 0.761 | 0.065 | 11.764 | 0.722 |
Anxiety | -> | GAD-7 Item 7 | 0.841 | 0.071 | 11.795 | 0.724 |
Depression | -> | PHQ-9 Item 1 | 1.000 | 0.746 | ||
Depression | -> | PHQ-9 Item 2 | 1.114 | 0.097 | 11.511 | 0.822 |
Depression | -> | PHQ-9 Item 3 | 1.027 | 0.108 | 9.482 | 0.690 |
Depression | -> | PHQ-9 Item 4 | 1.028 | 0.092 | 11.123 | 0.797 |
Depression | -> | PHQ-9 Item 5 | 1.076 | 0.110 | 9.780 | 0.709 |
Depression | -> | PHQ-9 Item 6 | 1.256 | 0.111 | 11.296 | 0.808 |
Depression | -> | PHQ-9 Item 7 | 0.838 | 0.094 | 8.866 | 0.648 |
Depression | -> | PHQ-9 Item 8 | 0.563 | 0.079 | 7.134 | 0.529 |
Depression | -> | PHQ-9 Item 9 | 0.708 | 0.080 | 8.874 | 0.649 |
Challenging goals | -> | LMF Item 7 | 1.000 | 0.794 | ||
Challenging goals | -> | LMF Item 6 | 1.141 | 0.105 | 10.846 | 0.874 |
Challenging goals | -> | LMF Item 5 | 0.798 | 0.086 | 9.280 | 0.688 |
Anxiety | -> | General fatigue | 0.081 | 0.120 | 0.677 | 0.071 |
Depression | -> | General fatigue | 0.989 | 0.158 | 6.254 | 0.725 |
Depression | <-> | Anxiety | 0.506 | 0.072 | 7.056 | 0.831 |
Depression | <-> | Social pressure and competition | −0.146 | 0.063 | −2.313 | −0.204 |
Social pressure and competition | <-> | Punishment | 0.491 | 0.126 | 3.891 | 0.382 |
Anxiety | <-> | Punishment | −0.091 | 0.089 | −1.016 | −0.083 |
Depression | <-> | Challenging goals | −0.339 | 0.069 | −4.906 | −0.491 |
Anxiety | <-> | Social pressure and competition | −0.170 | 0.074 | −2.290 | −0.199 |
Depression | <-> | Punishment | −0.090 | 0.075 | −1.187 | −0.097 |
Punishment | <-> | Challenging goals | 0.287 | 0.109 | 2.628 | 0.230 |
Social pressure and competition | <-> | Challenging goals | 0.300 | 0.093 | 3.217 | 0.311 |
Anxiety | <-> | Challenging goals | −0.324 | 0.075 | −4.296 | −0.393 |
Factors | Variables | B | SE | CR | β | |
---|---|---|---|---|---|---|
Punishment | -> | LMF Item 14 | 1.000 | 0.733 | ||
Punishment | -> | LMF Item 15 | 1.065 | 0.160 | 6.649 | 0.828 |
Social pressure and competition | -> | LMF Item 16 | 1.000 | 0.733 | ||
Social pressure and competition | -> | LMF Item 17 | 0.847 | 0.090 | 9.369 | 0.678 |
Social pressure and competition | -> | LMF Item 18 | 0.908 | 0.085 | 10.637 | 0.774 |
Social pressure and competition | -> | LMF Item 19 | 1.115 | 0.098 | 11.385 | 0.847 |
Anxiety | -> | GAD-7 Item 1 | 1.000 | 0.794 | ||
Anxiety | -> | GAD-7 Item 2 | 1.126 | 0.073 | 15.492 | 0.862 |
Anxiety | -> | GAD-7 Item 3 | 1.189 | 0.073 | 16.209 | 0.892 |
Anxiety | -> | GAD-7 Item 4 | 0.995 | 0.074 | 13.364 | 0.771 |
Anxiety | -> | GAD-7 Item 5 | 0.857 | 0.071 | 12.027 | 0.709 |
Anxiety | -> | GAD-7 Item 6 | 0.929 | 0.081 | 11.478 | 0.683 |
Anxiety | -> | GAD-7 Item 7 | 0.995 | 0.082 | 12.118 | 0.714 |
Depression | -> | PHQ-9 Item 1 | 1.000 | 0.773 | ||
Depression | -> | PHQ-9 Item 2 | 1.098 | 0.093 | 11.774 | 0.791 |
Depression | -> | PHQ-9 Item 3 | 1.051 | 0.115 | 9.110 | 0.631 |
Depression | -> | PHQ-9 Item 4 | 1.138 | 0.099 | 11.487 | 0.774 |
Depression | -> | PHQ-9 Item 5 | 1.047 | 0.103 | 10.185 | 0.697 |
Depression | -> | PHQ-9 Item 6 | 1.128 | 0.102 | 11.039 | 0.748 |
Depression | -> | PHQ-9 Item 7 | 0.807 | 0.096 | 8.386 | 0.586 |
Depression | -> | PHQ-9 Item 8 | 0.550 | 0.080 | 6.875 | 0.487 |
Depression | -> | PHQ-9 Item 9 | 0.561 | 0.081 | 6.911 | 0.490 |
Challenging goals | -> | LMF Item 7 | 1.000 | 0.858 | ||
Challenging goals | -> | LMF Item 6 | 1.061 | 0.065 | 16.352 | 0.911 |
Challenging goals | -> | LMF Item 5 | 0.895 | 0.061 | 14.555 | 0.815 |
Anxiety | -> | General fatigue | 0.443 | 0.081 | 5.478 | 0.348 |
Depression | -> | General fatigue | 0.335 | 0.091 | 3.668 | 0.251 |
Depression | <-> | Anxiety | 0.120 | 0.038 | 3.173 | 0.251 |
Depression | <-> | Social pressure and competition | −0.064 | 0.063 | 1.017 | -0.081 |
Social pressure and competition | <-> | Punishment | 0.739 | 0.149 | 4.951 | 0.555 |
Anxiety | <-> | Punishment | −0.008 | 0.064 | −0.124 | −0.010 |
Depression | <-> | Challenging goals | −0.214 | 0.060 | −3.588 | −0.294 |
Anxiety | <-> | Social pressure and competition | −0.004 | 0.063 | −0.068 | −0.005 |
Depression | <-> | Punishment | −0.038 | 0.064 | −0.593 | −0.050 |
Punishment | <-> | Challenging goals | 0.268 | 0.105 | 2.558 | 0.219 |
Social pressure and competition | <-> | Challenging goals | 0.424 | 0.106 | 3.982 | 0.334 |
Anxiety | <-> | Challenging goals | −0.128 | 0.057 | −2.230 | −0.168 |
Factors | Factors | B | SE | z | p | LL | UL | β | R2 | |
---|---|---|---|---|---|---|---|---|---|---|
Challenging goals | -> | Mental fatigue | −0.467 | 0.061 | −7.717 | <0.001 | −0.586 | −0.349 | −0.507 | 0.257 |
Mental fatigue | -> | Depression | 4.471 | 0.345 | 12.967 | <0.001 | 3.795 | 5.147 | 0.664 | 0.441 |
Depression | -> | Anxiety | 0.659 | 0.040 | 16.631 | <0.001 | 0.582 | 0.737 | 0.769 | 0.591 |
Factors | Factors | B | SE | z | p | LL | UL | β | Group * | |
---|---|---|---|---|---|---|---|---|---|---|
Challenging goals | -> | Mental fatigue | −0.467 | 0.061 | −7.717 | <0.001 | −0.586 | −0.349 | −0.507 | 2 |
Mental fatigue | -> | Depression | 4.471 | 0.345 | 12.967 | <0.001 | 3.795 | 5.147 | 0.664 | 2 |
Mental fatigue | -> | Anxiety | 3.262 | 0.335 | 9.726 | <0.001 | 2.605 | 3.920 | 0.565 | 2 |
Challenging goals | -> | Mental fatigue | −0.187 | 0.057 | −3.298 | <0.001 | −0.298 | −0.076 | −0.235 | 1 |
Mental fatigue | -> | Depression | 1.871 | 0.441 | 4.239 | <0.001 | 1.006 | 2.736 | 0.277 | 1 |
Mental fatigue | -> | Anxiety | 1.833 | 0.425 | 4.313 | <0.001 | 1.000 | 2.665 | 0.309 | 1 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Dirzyte, A.; Vijaikis, A.; Perminas, A.; Rimasiute-Knabikiene, R. Associations between Depression, Anxiety, Fatigue, and Learning Motivating Factors in e-Learning-Based Computer Programming Education. Int. J. Environ. Res. Public Health 2021, 18, 9158. https://doi.org/10.3390/ijerph18179158
Dirzyte A, Vijaikis A, Perminas A, Rimasiute-Knabikiene R. Associations between Depression, Anxiety, Fatigue, and Learning Motivating Factors in e-Learning-Based Computer Programming Education. International Journal of Environmental Research and Public Health. 2021; 18(17):9158. https://doi.org/10.3390/ijerph18179158
Chicago/Turabian StyleDirzyte, Aiste, Aivaras Vijaikis, Aidas Perminas, and Romualda Rimasiute-Knabikiene. 2021. "Associations between Depression, Anxiety, Fatigue, and Learning Motivating Factors in e-Learning-Based Computer Programming Education" International Journal of Environmental Research and Public Health 18, no. 17: 9158. https://doi.org/10.3390/ijerph18179158