Voices in the classroom: development and validation of an alternative scale for faculty evaluation

Authors

DOI:

https://doi.org/10.48017/dj.v9i3.3111

Keywords:

Exploratory Factor Analysis, Faculty Performance Evaluation, Mixed-Methods Approach, Student Surveys, Teaching Effectiveness

Abstract

This study presents a novel approach to evaluating faculty performance in the College of Education at Rizal Technological University through the development and validation of an alternative evaluation scale. As educational landscapes evolve, there is a critical need to adapt evaluation methods to align with current pedagogical trends and institutional goals. This research addresses these necessities by employing a mixed-methods approach that integrates qualitative insights from Focus Group Discussions with quantitative data gathered via student surveys. Through rigorous exploratory factor analysis, the study identifies and validates four key dimensions of faculty performance namely, Pedagogical Engagement and Relevance, Supportive Teaching Environment, Active Learning Facilitation, and Classroom Climate and Dynamics. Cronbach’s alpha and McDonald’s omega coefficients were employed to rigorously evaluate the reliability of each dimension, thereby ensuring consistent measurement. The findings highlight the importance of incorporating student perspectives to comprehensively evaluate teaching effectiveness and classroom dynamics. By capturing diverse aspects of faculty performance, including instructional strategies, student engagement facilitation, and classroom management practices, the developed scale provides a comprehensive tool for enhancing teaching quality and learning outcomes. The study's methodological rigor, anchored in measurement theory principles, enhances the validity and pertinency of the evaluation framework within the milieu of higher education. This research provides valuable insights and practical recommendations for educators, administrators, and policymakers aiming to create supportive and inclusive learning environments that enhance student success and faculty development.

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Author Biographies

Samuel A. Balbin, Rizal Technological University. Mandaluyong City, Metro Manila, Philippines

0000-0003-3844-1453. ; Rizal Technological University. Mandaluyong City, Metro Manila, Philippines. sabalbin@rtu.edu.ph

Faith Micah Abenes-Balbin, Rizal Technological University. Mandaluyong City, Metro Manila, Philippines

0009-0008-6086-2450. ; Rizal Technological University. Mandaluyong City, Metro Manila, Philippines. fmdmabenes@rtu.edu.ph

Wendelyn A. Samarita, Rizal Technological University. Mandaluyong City, Metro Manila, Philippines

 0000-0001-8234-9385. ; Rizal Technological University. Mandaluyong City, Metro Manila, Philippines. wasamarita@rtu.edu.ph

Vincent Anthony De Vera, Rizal Technological University. Mandaluyong City, Metro Manila, Philippines

 0009-0004-8634-1662. ; Rizal Technological University. Mandaluyong City, Metro Manila, Philippines. vadevera@rtu.edu.ph

Carina Nocillado, Rizal Technological University. Mandaluyong City, Metro Manila, Philippines

0009-0005-6135-7374. ; Rizal Technological University. Mandaluyong City, Metro Manila, Philippines. cnocillado@rtu.edu.ph

Liberty Gay Manalo, Rizal Technological University. Mandaluyong City, Metro Manila, Philippines

0000-0001-8909-838X. ; Rizal Technological University. Mandaluyong City, Metro Manila, Philippines. lgcmanalo@rtu.edu.ph

References

Acar, S., Savci, S., Keskinoğlu, P., Akdeniz, B., Özpelit, E., Özcan Kahraman, B., … Sevinc, C. (2016). Tampa Scale of Kinesiophobia for Heart Turkish Version Study: cross-cultural adaptation, exploratory factor analysis, and reliability. Journal of Pain Research, 9, 445–451. https://doi.org/10.2147/JPR.S105766

Al Maktoum, S. B., & Al Kaabi, A. M. (2024). Exploring teachers’ experiences within the teacher evaluation process: A qualitative multi-case study. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2023.2287931

Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., et al. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(3), 745–783. https://doi.org/10.1007/s10490-023-09871-y

Dawadi, S., Shrestha, S., & Giri, R. A. (2021). Mixed-Methods Research: A Discussion on its Types, Challenges, and Criticisms. Journal of Practical Studies in Education, 2(2), 25–36. https://doi.org/10.46809/jpse.v2i2.20

Di Leo, G., Sardanelli, F. (2020). Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach. Eur Radiol Exp 4, 18. https://doi.org/10.1186/s41747-020-0145-y

Draucker, C. B., Rawl, S. M., Vode, E., & Carter-Harris, L. (2020). Integration Through Connecting in Explanatory Sequential Mixed Method Studies. Western Journal of Nursing Research, 42(12), 1137–1147. https://doi.org/10.1177/0193945920914647

Fetters, M. D., & Tajima, C. (2022). Joint Displays of Integrated Data Collection in Mixed Methods Research. International Journal of Qualitative Methods, 21. https://doi.org/10.1177/16094069221104564

Hayes, A. F., & Coutts, J. J. (2020). Use Omega Rather than Cronbach’s Alpha for Estimating Reliability. But…. Communication Methods and Measures, 14(1), 1–24. https://doi.org/10.1080/19312458.2020.1718629

Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846–854. https://doi.org/10.1080/0142159X.2020.1755030

Kumar, A., Sarkar, M., Davis, E., et al. (2021). Impact of the COVID-19 pandemic on teaching and learning in health professional education: A mixed methods study protocol. BMC Medical Education, 21, 439. https://doi.org/10.1186/s12909-021-02871-w

Kumar, L., & Jana, S. K. (2022). Advances in absorbents and techniques used in wet and dry FGD: A critical review. Reviews in Chemical Engineering, 38(7), 843–880. https://doi.org/10.1515/revce-2020-0029

Lamm, K. W., Lamm, A. J., Davis, K., Sanders, C. E., & Powell, A. (2021). Perceptions of knowledge management capacity within extension services: An exploratory factor analysis approach. The Journal of Agricultural Education and Extension, 29(1), 53–74. https://doi.org/10.1080/1389224X.2021.1984956

Lee, J., Lim, C., & Kim, H. (2017). Development of an instructional design model for flipped learning in higher education. Educational Technology Research and Development, 65, 427–453. https://doi.org/10.1007/s11423-016-9502-1

McIntosh, A. R. (2021). Comparison of Canonical Correlation and Partial Least Squares analyses of simulated and empirical data. arXiv preprint arXiv:2107.06867. https://doi.org/10.48550/arXiv.2107.06867

Monteiro, V., Carvalho, C., & Santos, N. N. (2021). Creating a Supportive Classroom Environment Through Effective Feedback: Effects on Students’ School Identification and Behavioral Engagement. Frontiers in Education, 6, Article 661736. https://doi.org/10.3389/feduc.2021.661736

Murphy, K. R. (2020). Performance evaluation will not die, but it should. Human Resource Management Journal, 30(1), 13–31. https://doi.org/10.1111/1748-8583.12259

Nguyen, K. A., Borrego, M., Finelli, C. J., et al. (2021). Instructor strategies to aid implementation of active learning: A systematic literature review. International Journal of STEM Education, 8, 9. https://doi.org/10.1186/s40594-021-00270-7

Noor, S., Tajik, O., & Golzar, J. (2022). Simple random sampling. International Journal of Education & Language Studies, 1(2), 78–82. https://doi.org/10.22034/ijels.2022.162982

Pedler, M., Hudson, S., & Yeigh, T. (2020). The teachers’ role in student engagement: A review. Australian Journal of Teacher Education (Online), 45(3), 48–62. https://search.informit.org/doi/10.3316/ielapa.270830255864389

Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778. https://doi.org/10.1016/j.compedu.2019.103778

Ragupathi, K., & Lee, A. (2020). Beyond fairness and consistency in grading: The role of rubrics in higher education. In M. Ngo (Ed.), Diversity and Inclusion in Global Higher Education: Lessons from Across Asia (pp. 73–95). Springer. https://doi.org/10.1007/978-981-15-1628-3_3

Resende, M. D. V. D., & Alves, R. S. (2022). Statistical significance, selection accuracy, and experimental precision in plant breeding. Crop Breeding and Applied Biotechnology, 22(3), e42712238. https://doi.org/10.1590/1984-70332022v22n3a31

Schmidt, J. T., & Tang, M. (2020). Digitalization in Education: Challenges, Trends and Transformative Potential. In M. Harwardt, P. J. Niermann, A. Schmutte, & A. Steuernagel (Eds.), Führen und Managen in der digitalen Transformation (pp. 299–318). Springer Gabler. https://doi.org/10.1007/978-3-658-28670-5_16

Saeed, B., Tasmin, R., Mahmood, A., & Hafeez, A. (2022). Development of a multi-item Operational Excellence scale: Exploratory and confirmatory factor analysis. The TQM Journal, 34(3), 576–602. https://doi.org/10.1108/TQM-10-2020-0227

Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4–11. https://doi.org/10.12691/ajams-9-1-2

Shrestha, N., Poudel, A., & Limbong, T. (2018). Using Google form for student worksheet as learning media. International Journal of Engineering & Technology, 7(3.4), 321–324. https://doi.org/10.14419/ijet.v7i3.4.20125

Simanjuntak, B., & Limbong, T. (2018). Using Google form for student worksheet as learning media. International Journal of Engineering & Technology, 7(3.4), 321–324. https://doi.org/10.14419/ijet.v7i3.4.20125

Sürücü, L., Yıkılmaz, İ., & Maşlakçı, A. (2022). Exploratory factor analysis (EFA) in quantitative researches and practical considerations. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, 13(2), 947–965. https://doi.org/10.37989/gumussagbil.1183271

Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, 20, 679-686. https://doi.org/10.1016/S2212-5671(15)00123-9

Wilson, S. M., & Kelley, S. L. (2022). Landscape of teacher preparation programs and teacher candidates. National Academy of Education. https://files.eric.ed.gov/fulltext/ED618996.pdf

Wang, M.-T., Degol, J. L., Amemiya, J., Parr, A., & Guo, J. (2020). Classroom climate and children’s academic and psychological wellbeing: A systematic review and meta-analysis. Developmental Review, 57, Article 100912. https://doi.org/10.1016/j.dr.2020.100912

Xiao, L., & Hau, K.-T. (2023). Performance of Coefficient Alpha and Its Alternatives: Effects of Different Types of Non-Normality. Educational and Psychological Measurement, 83(1), 5-27. https://doi.org/10.1177/00131644221088240

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Published

2024-09-13

How to Cite

Balbin, S. A., Abenes-Balbin, F. M., Samarita, W. A., De Vera, V. A., Nocillado, C., & Manalo, L. G. (2024). Voices in the classroom: development and validation of an alternative scale for faculty evaluation. Diversitas Journal, 9(3). https://doi.org/10.48017/dj.v9i3.3111