The Positive Effects of Growth Mindset on Students’ Intention toward Self-Regulated Learning during the COVID-19 Pandemic: A PLS-SEM Approach
Abstract
:1. Introduction
2. Literature Review and Hypotheses
2.1. Growth Mindset
2.2. Self-Regulated Learning
2.3. Theory of Planned Behavior
2.4. Perceived Teacher Support
2.5. Development of Research Hypotheses and Conceptual Framework
3. Methodology
3.1. Participants and Procedure
3.2. Measures and Data Analysis
4. Results
4.1. Descriptive Analysis
4.2. Measurement Model Assessment
4.3. Structural Model Assessment
5. Discussion
5.1. Discussion of Findings
5.2. Limitations and Implications
5.2.1. Limitations
5.2.2. Implications
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Li, J.; Che, W. Challenges and coping strategies of online learning for college students in the context of COVID-19: A survey of Chinese universities. Sustain. Cities Soc. 2022, 83, 103958. [Google Scholar] [CrossRef]
- García-Morales, V.J.; Garrido-Moreno, A.; Martín-Rojas, R. The transformation of higher education after the COVID disruption: Emerging challenges in an online learning scenario. Front. Psychol. 2021, 12, 616059. [Google Scholar] [CrossRef]
- Jiang, Y.; Wang, P.; Li, Q.; Li, Y. Students’ Intention toward Self-Regulated Learning under Blended Learning Setting: PLS-SEM Approach. Sustainability 2022, 14, 10140. [Google Scholar] [CrossRef]
- Wang, P.; Zhao, P.; Li, Y. Design of Education Information Platform on Education Big Data Visualization. Wirel. Commun. Mob. Comput. 2022, 2022, 6779105. [Google Scholar] [CrossRef]
- Ku, Y.-R.; Stager, C. Rethinking the Multidimensionality of Growth Mindset Amid the COVID-19 Pandemic: A Systematic Review and Framework Proposal. Front. Psychol. 2022, 13, 572220. [Google Scholar] [CrossRef]
- Yao, Y.; Wang, P.; Jiang, Y.; Li, Q.; Li, Y. Innovative online learning strategies for the successful construction of student self-awareness during the COVID-19 pandemic: Merging TAM with TPB. J. Innov. Knowl. 2022, 7, 100252. [Google Scholar] [CrossRef]
- Zhao, H.; Xiong, J.; Zhang, Z.; Qi, C. Growth mindset and college students’ learning engagement during the COVID-19 pandemic: A serial mediation model. Front. Psychol. 2021, 12, 621094. [Google Scholar] [CrossRef] [PubMed]
- Dweck, C.S.; Chiu, C.-y.; Hong, Y.-y. Implicit theories and their role in judgments and reactions: A word from two perspectives. Psychol. Inq. 1995, 6, 267–285. [Google Scholar] [CrossRef][Green Version]
- Dweck, C.S.; Grant, H. Self-Theories, Goals, and Meaning. In Handbook of Motivation Science; Gardner, J.Y.S.W.L., Ed.; Guilford Press: New York, NY, USA, 2008. [Google Scholar]
- Carr, P.B.; Dweck, C.S. Intelligence and Motivation. In The Cambridge Handbook of Intelligence; Kaufman, R.J.S.S.B., Ed.; Cambridge University Press: Cambridge, UK, 2011; pp. 748–770. [Google Scholar]
- Haimovitz, K.; Dweck, C.S. The origins of children’s growth and fixed mindsets: New research and a new proposal. Child Dev. 2017, 88, 1849–1859. [Google Scholar] [CrossRef]
- Schunk, D.H. Social Cognitive Theory and Self-Regulated Learning. In Self-Regulated Learning and Academic Achievement; Springer: Berlin/Heidelberg, Germany, 1989; pp. 83–110. [Google Scholar]
- Ajzen, I. From Intentions to Actions: A Theory of Planned Behavior. In Action Control; Beckmann, J.K.a.J., Ed.; Springer: Berlin/Heidelberg, Germany, 1985; pp. 11–39. [Google Scholar]
- Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef][Green Version]
- Featherman, M.S.; Pavlou, P.A. Predicting e-services adoption: A perceived risk facets perspective. Int. J. Hum. Comput. Stud. 2003, 59, 451–474. [Google Scholar] [CrossRef][Green Version]
- Armitage, C.J.; Conner, M. Efficacy of the theory of planned behaviour: A meta-analytic review. Brit. J. Soc. Psychol. 2001, 40, 471–499. [Google Scholar] [CrossRef][Green Version]
- Sisk, V.F.; Burgoyne, A.P.; Sun, J.; Butler, J.L.; Macnamara, B.N. To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychol. Sci. 2018, 29, 549–571. [Google Scholar] [CrossRef]
- Ricard, N.C.; Pelletier, L.G. Dropping out of high school: The role of parent and teacher self-determination support, reciprocal friendships and academic motivation. Contemp. Educ. Psychol. 2016, 44, 32–40. [Google Scholar] [CrossRef]
- Wentzel, K.R.; Battle, A.; Russell, S.L.; Looney, L.B. Social supports from teachers and peers as predictors of academic and social motivation. Contemp. Educ. Psychol. 2010, 35, 193–202. [Google Scholar] [CrossRef]
- Ma, L.; Du, X.; Hau, K.-T.; Liu, J. The association between teacher-student relationship and academic achievement in Chinese EFL context: A serial multiple mediation model. Educ. Psychol. 2018, 38, 687–707. [Google Scholar] [CrossRef]
- Jampol, L.; Zayas, V. Gendered white lies: Women are given inflated performance feedback compared with men. Pers. Soc. Psychol. B 2021, 47, 57–69. [Google Scholar] [CrossRef]
- Skinner, E.A.; Belmont, M.J. Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. J. Educ. Psychol. 1993, 85, 571. [Google Scholar] [CrossRef]
- Blackwell, L.S.; Trzesniewski, K.H.; Dweck, C.S. Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Dev. 2007, 78, 246–263. [Google Scholar] [CrossRef] [PubMed]
- Chao, M.M.; Visaria, S.; Mukhopadhyay, A.; Dehejia, R. Do rewards reinforce the growth mindset? Joint effects of the growth mindset and incentive schemes in a field intervention. J. Exp. Psychol. Gen. 2017, 146, 1402. [Google Scholar] [CrossRef]
- Dweck, C.S. Mindset: The New Psychology of Success; Random House: New York, NY, USA, 2006. [Google Scholar]
- Burnette, J.L.; O’boyle, E.H.; VanEpps, E.M.; Pollack, J.M.; Finkel, E.J. Mind-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychol. Bull. 2013, 139, 655–701. [Google Scholar] [CrossRef][Green Version]
- Zeng, G.; Hou, H.; Peng, K. Effect of growth mindset on school engagement and psychological well-being of Chinese primary and middle school students: The mediating role of resilience. Front. Psychol. 2016, 7, 1873. [Google Scholar] [CrossRef][Green Version]
- Rhew, E.; Piro, J.S.; Goolkasian, P.; Cosentino, P. The effects of a growth mindset on self-efficacy and motivation. Congent Educ. 2018, 5, 1492337. [Google Scholar] [CrossRef]
- Yeager, D.S.; Hanselman, P.; Walton, G.M.; Murray, J.S.; Crosnoe, R.; Muller, C.; Tipton, E.; Schneider, B.; Hulleman, C.S.; Hinojosa, C.P. A national experiment reveals where a growth mindset improves achievement. Nature 2019, 573, 364–369. [Google Scholar] [CrossRef][Green Version]
- Rattan, A.; Savani, K.; Chugh, D.; Dweck, C.S. Leveraging mindsets to promote academic achievement: Policy recommendations. Perspect. Psychol. Sci. 2015, 10, 721–726. [Google Scholar] [CrossRef]
- de Carvalho, E.; Skipper, Y. A two-component growth mindset intervention for young people with SEND. J. Res. Spec. Educ. Needs 2020, 20, 195–205. [Google Scholar] [CrossRef]
- Zimmerman, B.J. Attaining Self-Regulation: A Social Cognitive Perspective. In Handbook of Self-Regulation; Boekaerts, M., Pintrich, P.R., Zeidner, M., Eds.; Academic Press: New York, NY, USA, 2000; pp. 13–39. [Google Scholar]
- Schunk, D.H.; Zimmerman, B.J. Self-Regulated Learning: From Teaching to Self-Reflective Practice; Guilford Press: New York, NY, USA, 1998. [Google Scholar]
- Pintrich, P.R. A conceptual framework for assessing motivation and self-regulated learning in college students. Educ. Psychol. Rev. 2004, 16, 385–407. [Google Scholar] [CrossRef][Green Version]
- Honicke, T.; Broadbent, J. The influence of academic self-efficacy on academic performance: A systematic review. Educ. Res. Rev. 2016, 17, 63–84. [Google Scholar] [CrossRef]
- Broadbent, J.; Poon, W.L. Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. Internet High Educ. 2015, 27, 1–13. [Google Scholar] [CrossRef]
- Mou, T.-Y. Online learning in the time of the COVID-19 crisis: Implications for the self-regulated learning of university design students. Act. Learn. High. Educ. 2021, 14697874211051226. [Google Scholar] [CrossRef]
- Hong, J.-C.; Lee, Y.-F.; Ye, J.-H. Procrastination predicts online self-regulated learning and online learning ineffectiveness during the coronavirus lockdown. Pers. Indiv. Differ. 2021, 174, 110673. [Google Scholar] [CrossRef] [PubMed]
- Cho, S.; Jang, S.J. Nursing students’ motivational and self-regulated learning during the COVID-19 pandemic: A cross-sectional study. Nurs. Health Sci. 2022, 24, 699–707. [Google Scholar] [CrossRef]
- Cai, R.; Wang, Q.; Xu, J.; Zhou, L. Effectiveness of students’ self-regulated learning during the COVID-19 pandemic. Sci. Insigt. 2020, 34, 175–182. [Google Scholar] [CrossRef]
- Taylor, S.; Todd, P. Assessing IT usage: The role of prior experience. MIS Quart. 1995, 19, 561–570. [Google Scholar] [CrossRef][Green Version]
- Kim, K.J.; Shin, D.-H. An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Res. 2015, 25, 527–541. [Google Scholar] [CrossRef]
- Alshebami, A.S.; Seraj, A.H.A.; Alzain, E. Lecturers’ Creativity and Students’ Entrepreneurial Intention in Saudi Arabia. Vision. 2022, 09722629221099596. [Google Scholar] [CrossRef]
- Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Dec. 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Bamberg, S.; Ajzen, I.; Schmidt, P. Choice of travel mode in the theory of planned behavior: The roles of past behavior, habit, and reasoned action. Basic. Appl. Soc. Psych. 2003, 25, 175–187. [Google Scholar] [CrossRef]
- Cheon, J.; Lee, S.; Crooks, S.M.; Song, J. An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Comput. Educ. 2012, 59, 1054–1064. [Google Scholar] [CrossRef]
- Valencia-Arias, A.; Chalela-Naffah, S.; Bermúdez-Hernández, J. A proposed model of e-learning tools acceptance among university students in developing countries. Educ. Inf. Technol. 2019, 24, 1057–1071. [Google Scholar] [CrossRef]
- Ruzek, E.A.; Hafen, C.A.; Allen, J.P.; Gregory, A.; Mikami, A.Y.; Pianta, R.C. How teacher emotional support motivates students: The mediating roles of perceived peer relatedness, autonomy support, and competence. Learn. Instr. 2016, 42, 95–103. [Google Scholar] [CrossRef][Green Version]
- Wentzel, K.R. Teacher-Student Relationships. In Handbook of Motivation at School; Wentzel, K., Miele, D.B., Eds.; Routledge: New York, NY, USA, 2016; pp. 211–230. [Google Scholar]
- Ryan, A.M.; Patrick, H. The classroom social environment and changes in adolescents’ motivation and engagement during middle school. Am. Educ. Res. J. 2001, 38, 437–460. [Google Scholar] [CrossRef]
- Hattie, J. Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement; Routledge: London, UK, 2008. [Google Scholar]
- Roorda, D.L.; Jak, S.; Zee, M.; Oort, F.J.; Koomen, H.M. Affective teacher–student relationships and students’ engagement and achievement: A meta-analytic update and test of the mediating role of engagement. Sch. Psychol. Rev. 2017, 46, 239–261. [Google Scholar] [CrossRef]
- Metheny, J.; McWhirter, E.H.; O’Neil, M.E. Measuring perceived teacher support and its influence on adolescent career development. J. Career Assess. 2008, 16, 218–237. [Google Scholar] [CrossRef]
- Dietrich, J.; Dicke, A.-L.; Kracke, B.; Noack, P. Teacher support and its influence on students’ intrinsic value and effort: Dimensional comparison effects across subjects. Learn. Instr. 2015, 39, 45–54. [Google Scholar] [CrossRef]
- Shih, S.-S. Perfectionism, implicit theories of intelligence, and Taiwanese eighth-grade students’ academic engagement. J. Educ. Res. 2011, 104, 131–142. [Google Scholar] [CrossRef]
- Da Fonseca, D.; Cury, F.; Fakra, E.; Rufo, M.; Poinso, F.; Bounoua, L.; Huguet, P. Implicit theories of intelligence and IQ test performance in adolescents with Generalized Anxiety Disorder. Behav. Res. Ther. 2008, 46, 529–536. [Google Scholar] [CrossRef]
- Mueller, C.M.; Dweck, C.S. Praise for intelligence can undermine children’s motivation and performance. J. Pers. Soc. Psychol. 1998, 75, 33. [Google Scholar] [CrossRef]
- Chen, J.A.; Pajares, F. Implicit theories of ability of Grade 6 science students: Relation to epistemological beliefs and academic motivation and achievement in science. Contemp. Educ. Psychol. 2010, 35, 75–87. [Google Scholar] [CrossRef]
- Shiau, W.-L.; Luo, M.M. Factors affecting online group buying intention and satisfaction: A social exchange theory perspective. Comput. Hum. Behav. 2012, 28, 2431–2444. [Google Scholar] [CrossRef]
- Raza, M.; Alyoussef, I.; Dahri, A.; Polyakova, A.; Alshebami, A.; Thomran, M. Effectiveness of entrepreneurship quality education in higher educational institutions: A mediating effect of entrepreneurial training. Manag. Sci. Lett. 2021, 11, 1221–1230. [Google Scholar] [CrossRef]
- Krejcie, R.V.; Morgan, D.W. Determining sample size for research activities. Educ. Psychol. Meas. 1970, 30, 607–610. [Google Scholar] [CrossRef]
- Mesler, R.M.; Corbin, C.M.; Martin, B.H. Teacher mindset is associated with development of students’ growth mindset. J. Appl. Dev. Psychol. 2021, 76, 101299. [Google Scholar] [CrossRef]
- Ajzen, I. Constructing a TPB Questionnaire: Conceptual and Methodological Considerations. 2006. Available online: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=0574b20bd58130dd5a961f1a2db10fd1fcbae95d (accessed on 24 December 2022).
- Ringle, C.M.; Wende, S.; Becker, J.-M. SmartPLS 3. SmartPLS GmbH, Boenningstedt. J. Serv. Sci. Manag. 2015, 10, 32–49. [Google Scholar]
- Briz-Ponce, L.; Pereira, A.; Carvalho, L.; Juanes-Méndez, J.A.; García-Peñalvo, F.J. Learning with mobile technologies—Students’ behavior. Comput. Hum. Behav. 2017, 72, 612–620. [Google Scholar] [CrossRef]
- Hair, J.; Hollingsworth, C.L.; Randolph, A.B.; Chong, A.Y.L. An updated and expanded assessment of PLS-SEM in information systems research. Ind. Manage. Data Syst. 2017, 1173, 442–458. [Google Scholar] [CrossRef]
- Hair, J.F.; Sarstedt, M.; Ringle, C.M. Partial Least Squares Structural Equation Modeling. In Handbook of Market Research; Homburg, C., Klarmann, M., Vomberg, A., Eds.; Springer: Cambridge, UK, 2012; pp. 587–632. [Google Scholar]
- Mehmood, S.M.; Najmi, A. Understanding the impact of service convenience on customer satisfaction in home delivery: Evidence from Pakistan. Int. J. Electron. Cust. Relatsh. Manag. 2017, 11, 23–43. [Google Scholar] [CrossRef]
- Kline, R.B. Principles and Practice of Structural Equation Modeling, 4th ed.; Guilford publications: New York, NY, USA, 2015. [Google Scholar]
- Claes Fornell, J.C. Partial Least Squares. In Advanced Marketing Research; Bagozzi, R., Ed.; Blackwell Publishing: Cambridge, UK, 1994; pp. 52–78. [Google Scholar]
- Gold, A.H.; Malhotra, A.; Segars, A.H. Knowledge management: An organizational capabilities perspective. J. Manage. Inform. Syst. 2001, 18, 185–214. [Google Scholar] [CrossRef]
- Podsakoff, P.M.; Organ, D.W. Self-reports in organizational research: Problems and prospects. J. Manag. 1986, 12, 531–544. [Google Scholar] [CrossRef]
- Chin, W.W. The Partial Least Squares Approach to Structural Equation Modeling. In Modern Methods for Business Research; Marcoulides, G.A., Ed.; Psychology Press: London, UK, 1998; Volume 295, pp. 295–336. [Google Scholar]
- Tenenhaus, M.; Vinzi, V.; Chatelin, Y.; Lauro, C. PLS path modeling. Comput. Stat. Data Anal. 2005, 48, 159–205. [Google Scholar] [CrossRef]
- Wetzels, M.; Odekerken-Schröder, G.; Van Oppen, C. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quart. 2009, 33, 177–195. [Google Scholar] [CrossRef]
- Denden, M.; Tlili, A.; Burgos, D.; Jemni, M.; Huang, R.; Essalmi, F.; Chang, T.-W. Framework for Teacher Support during Remote Teaching in a Crisis: COVID-19, as a Case Study. In Radical Solutions for Education in a Crisis Context; Burgos, D., Tlili, A., Tabacco, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2021; pp. 147–161. [Google Scholar]
- Suldo, S.M.; Friedrich, A.A.; White, T.; Farmer, J.; Minch, D.; Michalowski, J. Teacher support and adolescents’ subjective well-being: A mixed-methods investigation. Sch. Psychol. Rev. 2009, 38, 67–85. [Google Scholar] [CrossRef]
- Campbell, A.; Craig, T.; Collier-Reed, B. A framework for using learning theories to inform ‘growth mindset’activities. Int. J. Math. Educ. Sci. Technol. 2020, 51, 26–43. [Google Scholar] [CrossRef]
- Yeager, D.S.; Romero, C.; Paunesku, D.; Hulleman, C.S.; Schneider, B.; Hinojosa, C.; Lee, H.Y.; O’Brien, J.; Flint, K.; Roberts, A. Using design thinking to improve psychological interventions: The case of the growth mindset during the transition to high school. J. Educ. Psychol. 2016, 108, 374. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Cox, J.L.; Cheser, K.; Detwiler, J. A Triptych Study of the Impact of Teacher Dispositions on Teacher Hiring and Student Outcomes, Teacher and Student Growth Mindsets, and Student Perceptions of Teacher-Student Relationships; Northern Kentucky University: Highland Heights, KY, USA, 2015. [Google Scholar]
- Park, D.; Gunderson, E.A.; Tsukayama, E.; Levine, S.C.; Beilock, S.L. Young children’s motivational frameworks and math achievement: Relation to teacher-reported instructional practices, but not teacher theory of intelligence. J. Educ. Psychol. 2016, 108, 300. [Google Scholar] [CrossRef]
Dimensions | Questions | References |
---|---|---|
Growth Mindset | GM1: My intelligence is something that I can’t change very much. GM2: There are some things that I am not capable of learning. GM3: Challenging myself will not make me any smarter. GM4: If I am not naturally smart in a subject, I will never do well in it. | [62] |
Invested | INV1: My teachers expect me to work hard at school. INV2: My teachers try to answer my questions in my study time. INV3: My teachers are interested in my growth. INV4: My teachers take the time to help me get better grades. INV5: My teachers think I am a hard-working student. INV6: My teachers are helpful when I have questions about my studies. INV7: My teachers are helpful when I have questions about school issues. INV8: My teachers would praise me before others when I perform well at school. | |
Positive Regard | PR1: My teachers push me to gain good academic achievement. PR2: My teachers challenge me to think about my goals for my studies. PR3: My teachers believe I am smart so that I can study well by myself. PR4: My teachers help me understand my strengths in my studies. PR5: My teachers want me to do well in school. | [53] |
Expectation | EXP1: My teachers enjoy having me as their student. EXP2: My teachers care about what happens to me at school. EXP3: My teachers encourage me to learn. EXP4: My teachers think I should study continuously. EXP5: My teachers support my goals for my studies. | |
Accessible | ACC1: My teachers will listen if I want to talk about a problem in my studies. ACC2: My teachers are easy to talk to about my school things. ACC3: My teachers are easy to talk to about things beside school. | |
Attention | ATTEN1: I intend to do self-regulated learning to improve my academic achievements. ATTEN2: I intend to continue doing my self-regulated learning frequently. ATTEN3: I will strongly recommend my peers to do self-regulated learning. ATTEN4: I will always try to do self-regulated learning on a daily basis. ATTEN5: Overall, I intend to continue self-regulated learning in future learning. | |
Perceived Behavioral Control | PBC1: It is always possible for me to do my self-regulated learning. PBC2: If I want, I can always do self-regulated learning. PBC3: It is mostly up to me whether or not to do self-regulated learning. PBC4: I have control over how to do self-regulated learning. PBC5: I have the necessary knowledge to do self-regulated learning. | [44,63] |
Attitude | ATTI1: I Look forward to those aspects of self-regulated learning. ATTI2: I like self-regulated learning. ATTI3: Self-regulated learning is a good idea. ATTI4: I have a generally favorable attitude toward self-regulated learning. ATTI5: Overall, self-regulated learning is beneficial. | [11] |
Constructs | Items | Factor Loading | Cronbach’s Alpha | Composite Reliability | AVE |
---|---|---|---|---|---|
ATT | ATT1 | 0.930 | 0.964 | 0.972 | 0.874 |
ATT2 | 0.930 | ||||
ATT3 | 0.954 | ||||
ATT4 | 0.958 | ||||
ATT5 | 0.903 | ||||
ACC | ACC1 | 0.900 | 0.933 | 0.957 | 0.882 |
ACC2 | 0.958 | ||||
ACC3 | 0.959 | ||||
GM | GM1 | 0.873 | 0.911 | 0.934 | 0.738 |
GM2 | 0.872 | ||||
GM3 | 0.770 | ||||
GM4 | 0.909 | ||||
GM5 | 0.866 | ||||
BR | BR1 | 0.919 | 0.942 | 0.956 | 0.812 |
BR2 | 0.916 | ||||
BR3 | 0.931 | ||||
BR4 | 0.887 | ||||
BR5 | 0.848 | ||||
EXP | EXP1 | 0.850 | 0.916 | 0.937 | 0.749 |
EXP2 | 0.909 | ||||
EXP3 | 0.887 | ||||
EXP4 | 0.799 | ||||
EXP5 | 0.879 | ||||
INT | INT1 | 0.884 | 0.950 | 0.961 | 0.832 |
INT2 | 0.933 | ||||
INT3 | 0.902 | ||||
INT4 | 0.918 | ||||
INT5 | 0.924 | ||||
INV | INV1 | 0.824 | 0.943 | 0.953 | 0.717 |
INV2 | 0.852 | ||||
INV3 | 0.872 | ||||
INV4 | 0.863 | ||||
INV5 | 0.760 | ||||
INV6 | 0.871 | ||||
INV7 | 0.87 | ||||
INV8 | 0.858 | ||||
PBC | PBC1 | 0.878 | 0.942 | 0.956 | 0.812 |
PBC2 | 0.906 | ||||
PBC3 | 0.879 | ||||
PBC4 | 0.934 | ||||
PBC5 | 0.910 |
ACC | ATT | BR | EXP | INT | INV | GM | PBC | |
---|---|---|---|---|---|---|---|---|
ACC | 0.939 | |||||||
ATT | 0.494 | 0.935 | ||||||
BR | 0.839 | 0.515 | 0.901 | |||||
EXP | 0.862 | 0.518 | 0.808 | 0.865 | ||||
INT | 0.570 | 0.832 | 0.585 | 0.603 | 0.912 | |||
INV | 0.817 | 0.503 | 0.888 | 0.868 | 0.581 | 0.847 | ||
GM | 0.538 | 0.396 | 0.600 | 0.574 | 0.448 | 0.597 | 0.859 | |
PBC | 0.611 | 0.616 | 0.669 | 0.684 | 0.720 | 0.669 | 0.479 | 0.901 |
ACC | ATT | BR | EXP | INT | INV | GM | |
---|---|---|---|---|---|---|---|
ACC | |||||||
ATT | 0.522 | ||||||
BR | 0.895 | 0.541 | |||||
EXP | 0.832 | 0.552 | 0.876 | ||||
INT | 0.606 | 0.869 | 0.619 | 0.648 | |||
INV | 0.871 | 0.528 | 0.842 | 0.833 | 0.615 | ||
GM | 0.576 | 0.419 | 0.640 | 0.622 | 0.476 | 0.638 | |
PBC | 0.651 | 0.646 | 0.709 | 0.734 | 0.761 | 0.708 | 0.511 |
Hypothesis | Relationship | Original Sample | Standard Deviation | T Statistics | Decision |
---|---|---|---|---|---|
H1 | GM → INT | 0.176 *** | 0.047 | 3.742 | Supported |
H2 | GM → PBC→ INT | 0.109 *** | 0.020 | 5.514 | Supported |
H3 | GM → ATT → INT | 0.213 *** | 0.026 | 8.142 | Supported |
H4 | PTS →INT | 0.596 *** | 0.047 | 12.619 | Supported |
H5 | PTS → GM→ INT | 0.318 *** | 0.033 | 9.758 | Supported |
H6 | Moderation Effect of GM (PTS → INT) | 0.072 ** | 0.036 | 2.015 | Supported |
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Jiang, Y.; Liu, H.; Yao, Y.; Li, Q.; Li, Y. The Positive Effects of Growth Mindset on Students’ Intention toward Self-Regulated Learning during the COVID-19 Pandemic: A PLS-SEM Approach. Sustainability 2023, 15, 2180. https://doi.org/10.3390/su15032180
Jiang Y, Liu H, Yao Y, Li Q, Li Y. The Positive Effects of Growth Mindset on Students’ Intention toward Self-Regulated Learning during the COVID-19 Pandemic: A PLS-SEM Approach. Sustainability. 2023; 15(3):2180. https://doi.org/10.3390/su15032180
Chicago/Turabian StyleJiang, Yujun, Huying Liu, Yuna Yao, Qiang Li, and Yingji Li. 2023. "The Positive Effects of Growth Mindset on Students’ Intention toward Self-Regulated Learning during the COVID-19 Pandemic: A PLS-SEM Approach" Sustainability 15, no. 3: 2180. https://doi.org/10.3390/su15032180