Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute
Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute

Education Quarterly Reviews

ISSN 2621-5799

asia institute of research, journal of education, education journal, education quarterly reviews, education publication, education call for papers
asia institute of research, journal of education, education journal, education quarterly reviews, education publication, education call for papers
asia institute of research, journal of education, education journal, education quarterly reviews, education publication, education call for papers
asia institute of research, journal of education, education journal, education quarterly reviews, education publication, education call for papers
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Published: 18 July 2022

Systematic Study of Student’s Perception Towards Math through Teacher-Related Dimensions: A Case of COTs in Oman

Amina Al Jabri, Shija Gangadharan, Glorigem Bendanillo

UTAS Shinas, Sultanate of Oman

asia institute of research, journal of education, education journal, education quarterly reviews, education publication, education call for papers
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doi

10.31014/aior.1993.05.03.525

Pages: 68-77

Keywords: Structural Equation Modelling, Cronbach’s Alpha Reliability Test, Perception Towards Math, Omani Education

Abstract

Numerical explorations are accomplished to investigate the influence of student’s perception towards Math which advances into academic dismissal in the preliminary level of higher education. The population under study are the students of seven Colleges of Technology across Oman. Data analyses were done using descriptive statistics of frequency counts and percentages obtained from the research questionnaire, while the hypothesis was tested using the statistical tools at 0.05 level of significance. This study examines the student’s perception correlated with teacher-related factors. The structural equation modelling (SEM) is grounded on the three teacher-related dimensions that influence the student’s perception towards Math namely 4 attributes relating to personality traits of lecturers, 6 attributes for teaching skills of the lecturer, and 2 attributes for instructional material used by the lecturers to impart the Basic Math needs for higher education. This analysis shows an overall reliability analysis for teacher-related dimensions to be 0.943 by applying Cronbach’s alpha reliability test, and the SEM tool kit analysis of the model confirmed the hypothesis of the latent variables and the theoretical authenticity of the explored factors. The conclusions of this study might be useful to substantiate the importance of student evaluation on teachers done every semester and would be a component in reducing the dismissal of students at the initial stage of higher education in Oman.

References

  1. Carroll, M., Razvi, S. & Goodliffe, T.Using Foundation Program Academic Standards as a Quality Enhancement Tool. 2009 International Network for Quality Assurance Agencies in Higher Education (INQAAHE), 2009.

  2. Sivaraman, I., Hassan Mohammed Al Balushi, A., Rao, D. H., & Rizwan, S. M., “Entry into Engineering Programs – Performance Comparison of Foundation Students with Direct Entry Students.” 7, 2012.

  3. Alami, M,. “Causes of Poor Academic Performance among Omani Students. International Journal of Social Science Research”, 4(1), 126–136, 2016. https://doi.org/10.5296/ijssr.v4i1.8948

  4. Martin, K., Galentino, R., & Townsend, L,. “Community College Student Success: The Role of Motivation and Self-Empowerment”. Community College Review, 42(3), 221–241, 2014. https://doi.org/10.1177/0091552114528972

  5. Coleman, B., & McNeese, M. N. “From home to school: The relationship among parental involvement, student motivation, and academic achievement”. 16, 459–470, 2009.

  6. Wilder, S., “Effects of parental involvement on academic achievement: A meta-synthesis”. Educational Review, https://doi.org/10.1080/00131911.2013.780009, 66(3), 377–397 ,2014.

  7. Al-Mahrooqi, R,. “A Student Perspective on Low English Proficiency in Oman. International Education Studies”, 5(6), 263–271, 2012.

  8. Ambussaidi, Intisar & Yang, Ya-Fei. (2019). The Impact of Mathematics Teacher Quality on Student Achievement in Oman and Taiwan. International Journal of Education and Learning. 1. 50-62. 10.31763/ijele.v1i2.39.

  9. Hill, H. C., Rowan, B., & Ball, D. L.” Effects of Teachers’ Mathematical Knowledge for Teaching on Student Achievement”. 36, 2005.

  10. Jürgen Baumert, Mareike Kunter, Werner Blum, Martin Brunner, Thamar Voss, Alexander Jordan, Uta Klusmann, Stefan Krauss, Michael Neubrand, Yi-Miau Tsai, “Teachers’ Mathematical Knowledge, Cognitive Activation in the Classroom, and Student Progress”, 2010.

  11. Hair, J. F., Black, B., Babin, B., Anderson, R. E., & Tatham, R. L. Multivariate Data Analysis, 6th Edition (6th ed.), 2006. Pearson Prentice Hall.

  12. Nunnally, J. C. Psychometric Theory (2nd ed.), 1978. McGraw-Hill.

  13. Lance, C. E., Butts, M. M., & Michels, L. C. The Sources of Four Commonly Reported Cutoff Criteria: What Did They Really Say? Organizational Research Methods, 9(2), 202–220, 2006. https://doi.org/10.1177/1094428105284919

  14. Kaiser, H. F., & Rice, J. Little Jiffy, Mark Iv. Educational and Psychological Measurement, 34(1), 111–117, 1974. https://doi.org/10.1177/001316447403400115

  15. Ullman, J. B., & Bentler, P. M. (2012). Structural Equation Modeling. In Handbook of psychology, Second Edition. John Wiley & Sons, Ltd., 2012.

  16. Anderson, J. C., & Gerbing, D. W. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423, 1988. https://doi.org/10.1037/0033-2909.103.3.411

  17. Tobbin, P. Adoption of Mobile Money Transfer Technology: Structural Equation Modeling Approach. European Journal of Business and Management, 3(7), 65, 2011.

  18. Schumacker, R. E., & Lomax, R. G. A beginner’s guide to structural equation modeling (pp. xvi, 288), 1996. Lawrence Erlbaum Associates, Inc.

  19. Gerbing, D. W., & Anderson, J. C. Monte Carlo Evaluations of Goodness of Fit Indices for Structural Equation Models. Sociological Methods & Research, 21(2), 132–160, 1992.

  20. Hu, L., & Bentler, P. M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55, 1999. https://doi.org/10.1080/10705519909540118

  21. Bagozzi, R. P., & Yi, Y. On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94, 1988. https://doi.org/10.1007/BF02723327

  22. Hooper, D., Coughlan, J., & Mullen, M. Structural Equation Modelling: Guidelines for Determining Model Fit. Articles, 2008. https://doi.org/10.21427/D7CF7R

  23. Nor Fadilah Tahar, Zuriati Ismail, Nur Diana Zamani, Norshaieda Adnan, (2010). Students’ Attitude Toward Mathematics: The Use of Factor Analysis in Determining the Criteria, Procedia - Social and Behavioral Sciences, 8, 476-481, 2010. ISSN 1877-0428.

  24. Aziz, S. M,. Dimensions of IT literacy in an Arab region: A study in Barkha (Oman). 2009 International Conference on Information and Communication Technologies and Development (ICTD), 288–299, 2009. https://doi.org/10.1109/ICTD.2009.5426675

  25. Bransford, J. D., Brown, A. L., Cocking, R. R., Donovan, M. S., & Pellegrino, J. W. How People Learn. Washington, D.C.: National Academy Press, 2004.

  26. Mallet, D. G. “Walking a Mile in Their Shoes: Non-Native English”, Speakers’ Difficulties in English Language Mathematics Classrooms. Journal of Learning Design, 4(3), 28–34, 2011.

  27. Neville-Barton, Pip, Bill Barton, Bill., “The Relationship between English Language and Mathematics Learning for Non-native Speaker”. New Zealand, Wellington, 2005.

  28. Sergon, V. “Playing the Blame Game: English Education in Omani Government Schools”. 37, 2011.