Influence of Classroom Management on Intermediate Level Students’ Academic Achievements in Aligarh District of Uttar Pradesh in India
Downloads
This study investigates the impact of classroom management on student academic achievement at the intermediate level in Aligarh District, Uttar Pradesh, India, using Structural Equation Modeling-Partial Least Squares (SEM-PLS). A sample of 383 intermediate-level teachers was randomly selected, and student achievement data were obtained from final-year graduates. The study aims to assess the relationships between key classroom management factors—such as classroom rules, discipline methods, reward systems, and the teaching-learning process—and their influence on academic performance. SEM-PLS was employed to analyze the hypothesized model, evaluate the structural relationships between the variables, and validate measurement indicators. The results show a strong positive relationship between effective classroom management and student achievement. Key factors such as well-defined classroom rules, effective discipline, and reward systems were found to significantly influence student engagement and academic success. The model fit indices confirm the robustness of the proposed relationships. This research highlights the importance of structured classroom management in fostering student achievement and provides a comprehensive understanding of how classroom directives, rewards, and disciplinary methods contribute to learning outcomes. The findings suggest potential pathways for educational improvements and recommend further research to explore these dynamics across different educational settings using SEM-PLS.
Downloads
1. W. Lawsha, M. & Hussain, “Secondary Students’ Attitude towards Mathematics in a Selected School of Maldives,” Int. J. Humanit. Soc. Sci., vol. 1, no. 15, 2011.
2. X. Bi, “Associations between psychosocial aspects of English classroom environments and motivation types of Chinese tertiary-level English majors,” Learn. Environ. Res., vol. 18, no. 1, 2015, doi: 10.1007/s10984-015-9177-2.
3. E. S.-P. Bulbin Socuoglu, Selma Akalin, “The Effects of Classroom Management on the Behaviors of Students with Disabilities in Inclusive Classrooms in Turkey,” J. Int. Assoc. Spec. Educ., vol. 11, no. 1, 2010.
4. K. Sieberer-Nagler, “Effective Classroom-Management & Positive Teaching,” English Lang. Teach., vol. 9, no. 1, 2015, doi: 10.5539/elt.v9n1p163.
5. B. T. Afalla and F. L. Fabelico, “SUSTAINING ACADEMIC SUCCESS THROUGH EFFECTIVE CLASSROOM MANAGEMENT,” Humanit. Soc. Sci. Rev., vol. 8, no. 4, 2020, doi: 10.18510/hssr.2020.8422.
6. C. N. Che Ahmad, S. A. Shaharim, and M. F. N. L. Abdullah, “Teacher-student interactions, learning commitment, learning environment and their relationship with student learning comfort,” J. Turkish Sci. Educ., vol. 14, no. 1, 2017, doi: 10.12973/tused.10190a.
7. A.-O. Ebimiere, N. J. Uloaku, & Nweke, and E. Onyekachi, “CLASSROOM MANAGEMENT AND STUDENTS ACADEMIC PERFORMANCE IN PUBLIC SECONDARY SCHOOLS IN RIVERS STATE,” 2020.
8. J. Sinclair, K. C. Herman, W. M. Reinke, N. Dong, and M. Stormont, “Effects of a Universal Classroom Management Intervention on Middle School Students With or At Risk of Behavior Problems,” Remedial Spec. Educ., vol. 42, no. 1, 2021, doi: 10.1177/0741932520926610.
9. S. R. Self-brown and S. Mathews, “Effects of classroom structure on student achievement goal orientation,” J. Educ. Res., vol. 97, no. 2, 2003, doi: 10.1080/00220670309597513.
10. R. H. Malik and A. Rizvi, “Effect of Classroom Learning Environment on Students’ Academic Achievement in Mathematics at Secondary Level,” Bull. Educ. Res., vol. 40, no. 2, 2018.
11. S. B. Flory, C. Nieman, and R. C. Wylie, “Facilitating culturally responsive teaching in PETE alumni: a mixed methods analysis,” Curric. Stud. Heal. Phys. Educ., vol. 15, no. 1, 2024, doi: 10.1080/25742981.2023.2208566.
12. M. Kunter, J. Baumert, and O. Köller, “Effective classroom management and the development of subject-related interest,” Learn. Instr., vol. 17, no. 5, 2007, doi: 10.1016/j.learninstruc.2007.09.002.
13. M. Sandelowski, “Focus on research methods: Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed-method studies,” Res. Nurs. Heal., vol. 23, no. 3, 2000, doi: 10.1002/1098-240x(200006)23:3<246::aid-nur9>3.0.co;2-h.
14. J. Zangirolami-Raimundo, J. de O. Echeimberg, and C. Leone, “Research methodology topics: Cross-sectional studies,” J. Hum. Growth Dev., vol. 28, no. 3, 2018, doi: 10.7322/jhgd.152198.
15. A. Al-Aufi and H. Al-Azri, “Information literacy in Oman’s higher education: A descriptive-inferential approach,” J. Librariansh. Inf. Sci., vol. 45, no. 4, 2013, doi: 10.1177/0961000613486824.
16. A. Singh and M. Masuku, “Sampling Techniques & Determination of Sample Size in Applied Statistics Research: an Overview,” Ijecm.Co.Uk, vol. II, no. 11, 2014.
17. T. D. Nguyen, M. H. Shih, D. Srivastava, S. Tirthapura, and B. Xu, “Stratified random sampling from streaming and stored data,” Distrib. Parallel Databases, vol. 39, no. 3, 2021, doi: 10.1007/s10619-020-07315-w.
18. T. Zaman, “An efficient exponential estimator of the mean under stratified random sampling,” Math. Popul. Stud., vol. 28, no. 2, 2021, doi: 10.1080/08898480.2020.1767420.
19. C. R. Howell, W. Su, A. F. Nassel, A. A. Agne, and A. L. Cherrington, “Area based stratified random sampling using geospatial technology in a community-based survey,” BMC Public Health, vol. 20, no. 1, 2020, doi: 10.1186/s12889-020-09793-0.
20. A. Leguina, “A primer on partial least squares structural equation modeling (PLS-SEM),” Int. J. Res. Method Educ., vol. 38, no. 2, 2015, doi: 10.1080/1743727x.2015.1005806.
21. C. Barroso, G. C. Carrión, and J. L. Roldán, “Applying Maximum Likelihood and PLS on Different Sample Sizes: Studies on SERVQUAL Model and Employee Behavior Model,” in Handbook of Partial Least Squares, 2010. doi: 10.1007/978-3-540-32827-8_20.
22. S. Benzidia, N. Makaoui, and O. Bentahar, “The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance,” Technol. Forecast. Soc. Change, vol. 165, 2021, doi: 10.1016/j.techfore.2020.120557.
23. J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to use and how to report the results of PLS-SEM,” 2019. doi: 10.1108/EBR-11-2018-0203.
24. M. Sabol, J. Hair, G. Cepeda, J. L. Roldán, and A. Y. L. Chong, “PLS-SEM in information systems: seizing the opportunity and marching ahead full speed to adopt methodological updates,” 2023. doi: 10.1108/IMDS-07-2023-0429.
25. J. Rowley, “Designing and using research questionnaires,” Manag. Res. Rev., vol. 37, no. 3, 2014, doi: 10.1108/MRR-02-2013-0027.
26. J. C. F. Ho and M. Z. Yao, “Sequence analysis in distributed interactive learning environments: Visualization and clustering of exploratory behavior,” J. Educ. Online, vol. 15, no. 2, 2018, doi: 10.9743/jeo.2018.15.2.10.
27. V. Braun, V. Clarke, E. Boulton, L. Davey, and C. McEvoy, “The online survey as a qualitative research tool,” Int. J. Soc. Res. Methodol., vol. 24, no. 6, 2021, doi: 10.1080/13645579.2020.1805550.
28. S. McLeod, “Likert Scale Definition, Examples and Analysis,” Simply Psychol., 2019.
29. E. Weyant, “Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th Edition,” J. Electron. Resour. Med. Libr., vol. 19, no. 1–2, 2022, doi: 10.1080/15424065.2022.2046231.
30. C. M. Ateh and L. B. Ryan, “Preparing teacher candidates to be culturally responsive in classroom management,” Soc. Sci. Humanit. Open, vol. 7, no. 1, 2023, doi: 10.1016/j.ssaho.2023.100455.
Copyright (c) 2024 Tariq Zubair, Uzma Qazi
This work is licensed under a Creative Commons Attribution 4.0 International License.
