Unveiling Teaching Quality: A Hybrid Approach with Factor Analysis and Machine Learning
Keywords:
factor analysis, correlation matrix, reliability, validity, machine-learning, ensemble, feedback system, bartlett’s test, SHapley Additive exPlanationsAbstract
This study investigates the teaching effectiveness within the educational institution utilizing machine learning models and factor analysis methods. Feedback data collected from students is analyzed to predict faculty performance, employing algorithms such as Lasso, Ridge, Decision Tree, Random Forest, etc. Yielding R2 test score of 0.94 with the weighted average ensemble model. Additionally, factor analysis is employed to uncover underlying constructs influencing teaching quality with correlation matrix and reliability and validity being examined using KMO and Bartlett’s Test. This is also verified with Principal Component Analysis and Verimax Rotated solution. Results showcase the predictive capabilities of machine learning models and offer insights into the multifaceted factors shaping student perceptions of faculty performance. The integration of diverse analytical techniques provides a comprehensive framework for assessing and enhancing teaching effectiveness.
Downloads
References
Carless, D. (2015a). Excellence in university assessment: Learning from award-winning practice. London: Routledge.
Henderson, M., Ajjawi, R., Boud, D., & Molloy, E. (forthcoming, 2019). Feedback that makes a difference. In M. Henderson, R. Ajjawi, D. Boud, & E. Molloy (Eds.), The impact of feedback in higher education. London: Palgrave Macmillan.
Hattie, J and Timperley (2007). The power of feedback. Educational research, 77(1), pp.81-112.
Graham Gibbs(2006). How assessment frames student learning in innovative assessment in higher education.(Editors): Cordelia Bryan and Karen Clegg, Routledge (Taylor & Francis group), London and New York, pp.23-36
Margaret Price and Berry O’Donovan (2006). Improve performance through enhancing student understanding of criteria and feedback in Innovative Assessment in higher education. (Editors): Cordelia Bryan and Karen Clegg, Routledge (Taylor & Francis group), London and New York, pp.100-109.
David Carless, Diane Salter, Ming Yang and Joy Lam (2011). Developing sustainable feedback practices, studies in higher education, Volume 36(4), pp.395-407
Michael Barth(2008). Deciphering student evaluation of teaching: A Factor analysis approach. Journal of education for business, Volume 84(1),pp. 40-46. Doi 10.3200/JOEB.84.1.40-46
David J Nicol and Debra Macfarlane-Dick (2006). Formative assessment and self-regulated learning: A Model of seven principles of good feedback practice. Studies in Higher Education. Vol 31(2), pp.199-218.
Boud, D and Molloy, E (2013). Rethinking models of feedback learning: The Challenge of Designing Assessment and Evaluation in Higher Education. Volume 38(6), pp 698-712.
Mirza Anwar Zainuddin, Lai Jun Jer, Muhammad Zulhelme Bakar, Kumaresan Palanisamy and Zahaya Md Yousof (2021). Evaluation of student’s Satisfaction Towards Instructor Using Factor Analysis. Journals of Science and Mathematics Letters. Volume 9 (1), pp.32-45.
Aldoseri A., K. N. A. - Khalifa, and A. M. Hamouda, “Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges,” Applied Sciences, vol. 13, no. 12, pp. 7082–7082, Jun. 2023, doi: https://doi.org/10.3390/app13127082
Davide Cacciarelli and Murat Külahçı, “Active learning for data streams: a survey,” Machine Learning, Nov. 2023, doi: https://doi.org/10.1007/s10994-023-06454-2
O. A. Montesinos López, A. Montesinos López, and J. Crossa, “Overfitting, Model Tuning, and Evaluation of Prediction Performance,” Multivariate Statistical Machine Learning Methods for Genomic Prediction, pp. 109–139, 2022, doi: https://doi.org/10.1007/978-3-030-89010-0_4
D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Applied Soft Computing, vol. 97, p. 105524, May 2019, doi: https://doi.org/10.1016/j.asoc.2019.105524
L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, Nov. 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061
G. C. Cawley and N. L. C. Talbot, “On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation,” Journal of Machine Learning Research, vol. 11, no. 70, pp. 2079–2107, 2010, Accessed: Apr. 29, 2024. [Online]. Available: https://www.jmlr.org/papers/v11/cawley10a.html
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Computer Science, vol. 7, no. 5, p. e623, Jul. 2021, doi: https://doi.org/10.7717/peerj-cs.623
S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” arXiv.org, Nov. 24, 2017. https://arxiv.org/abs/1705.07874v2
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
