The Impact of A Supportive School Culture and Of Teachers’ Professional Collaboration, Work Environment Satisfaction, and Professional Satisfaction on Student Motivation
Özet
This study by Rahmi Baki investigates the structural relationships between a supportive school culture (SSC), professional collaboration (PC), work environment satisfaction (WES), professional satisfaction (PS), and student motivation (SM). Grounded in educational and psychological literature, the proposed structural equation model posits that a supportive school environment enhances teachers' satisfaction with their work environment, while collaborative practices among teachers boost their overall professional satisfaction. Consequently, both work environment satisfaction and professional satisfaction positively influence student learning motivation. To test these hypotheses, empirical data were gathered between October and December 2021 from 308 primary, middle, and high school teachers using a validated two-part questionnaire. The data were evaluated using a two-stage structural equation modeling approach with SPSS AMOS 22 software. The empirical findings fully supported all four proposed hypotheses without requiring model modifications. Specifically, a supportive school culture positively impacts work environment satisfaction, and professional collaboration significantly enhances professional satisfaction. Ultimately, both work environment satisfaction and professional satisfaction demonstrate a direct, positive, and statistically significant effect on student motivation. The research underscores that fostering mutual respect, open communication, shared goals, and collaborative professional relationships within educational institutions directly contributes to higher teacher job satisfaction, ultimately driving improved student engagement and learning outcomes.
Referanslar
Ahmad, I., & Ahmad, S. B. (2019). The mediation effect of strategic planning on the relationship between business skills and firm’s performance: Evidence from medium enterprises in Punjab, Pakistan. Opción: Revista de Ciencias Humanas y Sociales, (24), 746-778.
Al Ansari, H., Ward, R., & Hill, R. (2021, July). Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK. In 2021 International Conference on Advanced Learning Technologies (ICALT), 442-444.
Atik, S., & Çelik, O. T. (2021). Analysis of the relationships between academic motivation, engagement, burnout and academic achievement with structural equation modelling. International Journal of Contemporary Educational Research, 8(2), 118-130.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Brandmiller, C., Dumont, H., & Becker, M. (2020). Teacher perceptions of learning motivation and classroom behavior: the role of student characteristics. Contemporary Educational Psychology, 63, 101893.
Burić, I., & Kim, L. E. (2020). Teacher self-efficacy, instructional quality, and student motivational beliefs: An analysis using multilevel structural equation modeling. Learning and Instruction, 66, 101302.
Corrigan, P. W., Edwards, A. B., Green, A., Diwan, S. L., & Penn, D. L. (2001). Prejudice, social distance, and familiarity with mental illness. Schizophrenia bulletin, 27(2), 219-225.
Dermitzaki, I., & Stavroussi, P. (2012). Adaptation of the Students' Motivation towards Science Learning (SMTSL) questionnaire in the Greek language. European Journal of Psychology of Education, 28 (3), 747-766.
Eccles J. S., & Wigfield A. (2002). Motivational beliefs, values, and goals. Review of Psychology, 53 (1), 109-132.
Elster, D. (2010). Learning Communities in Teacher Education: The impact of e‐competence. International Journal of Science Education, 32(16), 2185-2216.
Faridah, I., Sari, F. R., Wahyuningsih, T., Oganda, F. P., & Rahardja, U. (2020, October). Effect Digital Learning on Student Motivation during Covid-19. In 2020 8th International Conference on Cyber and IT Service Management (CITSM), 1-5.
Furió, D., Juan, M. C., Seguí, I., & Vivó, R. (2015). Mobile learning vs. traditional classroom lessons: a comparative study. Journal of Computer Assisted Learning, 31(3), 189-201.
George, D., & Mallery, P. (2003). SPSS for Windows Step by Step: A Simple Guide and Reference. Boston: Allyn & Bacon.
Henry, J. W., & Stone, R. W. (1994). A structural equation model of end-user satisfaction with a computer-based medical information system. Information Resources Management Journal (IRMJ), 7(3), 21-33.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Kaluzeviciute, G., Jessiman, T., Burn, A. M., Ford, T., Geijer–Simpson, E., Kidger, J., ... & Spencer, L. (2021). Participatory action research on school culture and student mental health: A study protocol. International Journal of Qualitative Methods, 20, 1-13.
Koşar, S., & Çalık, T. (2011). Okul yöneticilerinin yönetimde gücü kullanma stilleri ile örgüt kültürü arasındaki ilişki. Kuram ve Uygulamada Eğitim Yönetimi, 4(4), 581-603.
Kumar, C. S., & Sangeetha, T. (2019). A study on relationship between achievement motivation and academic achievement in English among high school students. International Journal of Trend in Scientific Research and Development, 3(4), 1343-1348.
Levesque-Bristol, C., Richards, K. A. R., Zissimopoulos, A., Wang, C., & Yu, S. (2020). An evaluation of the integrative model for learning and motivation in the college classroom. Current Psychology, 39, 1-13.
Liu, Y., Bellibaş, M. Ş., & Gümüş, S. (2021). The effect of instructional leadership and distributed leadership on teacher self-efficacy and job satisfaction: Mediating roles of supportive school culture and teacher collaboration. Educational Management Administration & Leadership, 49(3), 430-453.
Locke E, A., (1976). The nature and causes of job satisfaction. In: M.D. Dunnette (ed) Handbook of Industrial and Organisational Psychology. Chicago: Rand McNally, 1297–1349.
Martin, A. J. (2001). The Student Motivation Scale: A tool for measuring and enhancing motivation. Journal of Psychologists and Counsellors in Schools, 11, 1-20.
Misbah, Z., Gulikers, J., Widhiarso, W., & Mulder, M. (2021). Exploring connections between teacher interpersonal behaviour, student motivation and competency level in competence-based learning environments. Learning Environments Research, 1-21.
Morgan, G. (2011). IBM SPSS for introductory statistics (4th ed.). New York: Routledge.
Nathanaili, V. (2017). Teacher-teacher and teacher-principal collaborations in the public schools of Albanian educational pre-university system. ILIRIA International Review, 7(1), 82-98.
Nukpe, P. (2012). Motivation: theory and use in Higher Education. Investigations in University Teaching and Learning, 8, 11-17.
Palmer, D. (2007). What is the best way to motivate students in science? Teaching Science, 53(1), 38-42.
Pepe, A., Addimando, L., & Veronese, G. (2017). Measuring teacher job satisfaction: Assessing invariance in the teacher job satisfaction scale (TJSS) across six countries. Europe's Journal of Psychology, 13(3), 396-416.
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of educational Psychology, 95(4), 667-686.
Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0: An Updated Guide and Practical Guide to Statistical Analysis. Kuala Lumpur: Pearson
Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling Psychology. Lawrence Erlbaum Associates, Mahwah, NJ.
Scrima, F., Moffat, E., & Rioux, L. (2015). Italian Validation of the Scale of Satisfaction with the Work Environment. BPA-Applied Psychology Bulletin (Bollettino di Psicologia Applicata), 63(274), 35-48.
Singh, R., Sandhu, H. S., Metri, B. A., & Kaur, R. (2011). Organizational performance and retail challenges: a structural equation approach. iBusiness, 3(02), 159-168.
Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of educational psychology, 85(4), 571-581.
Terosky, A. L., & Reitano, M. C. (2016). Putting followers first: The role of servant leadership in cases of urban, public school principals. Journal of School Leadership, 26(2), 192-222.
Tsai, P., & Antoniou, P. (2021). Teacher job satisfaction in Taiwan: making the connections with teacher attitudes, teacher self-efficacy and student achievement. International Journal of Educational Management, 35, 51016-1029.
Williams, K. C., & Williams, C. C. (2011). Five key ingredients for improving student motivation. Research in Higher Education Journal, 12, 1-23.
Winberg, M., & Palm, T. (2021). Antecedents and Relative Importance of Student Motivation for Science and Mathematics Achievement in TIMSS. Frontiers in Education, 6(97), 1-21.
Xiong, Y., Li, H., Kornhaber, M. L., Suen, H. K., Pursel, B., & Goins, D. D. (2015). Examining the relations among student motivation, engagement, and retention in a MOOC: A structural equation modeling approach. Global Education Review, 2(3), 23-33.
Xu, H., & Gao, Y. (2014). The development of English learning motivation and learners' identities: A structural equation modeling analysis of longitudinal data from Chinese universities. System, 47, 102-115.
Yu, R., & Singh, K. (2018). Teacher support, instructional practices, student motivation, and mathematics achievement in high school. The Journal of Educational Research, 111(1), 81-94.
