Ranking Learning Algorithm for Likert Scale (RLALS) for Prediction of Student Perceptions about Curriculum, Teaching-Learning and Research

Ranking Learning Algorithm for Likert Scale (RLALS) for Prediction of Student Perceptions about Curriculum, Teaching-Learning and Research
  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-8
Year of Publication : 2024
Author : Dharmendra Patel, Sanskruti Patel, Atul Patel, Jay Nanavati, Unnati Patel, Ankit Faldu, Amisha Shingala
DOI : 10.14445/22315381/IJETT-V72I8P123

How to Cite?

Dharmendra Patel, Sanskruti Patel, Atul Patel, Jay Nanavati, Unnati Patel, Ankit Faldu, Amisha Shingala,"Ranking Learning Algorithm for Likert Scale (RLALS) for Prediction of Student Perceptions about Curriculum, Teaching-Learning and Research," International Journal of Engineering Trends and Technology, vol. 72, no. 8, pp. 237-243, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I8P123

Abstract
The Likert scale is an important aspect of collecting data for various situations. The data is in ordinal form, so performing analysis and prediction requires a special kind of algorithm. In this paper, a ranking learning algorithm for the Likert Scale (RLALS) is proposed to predict ordinal data. The data from the education domain is collected for experimentation. The data related to the feedback process in the context of curriculum, teaching-learning, and research was collected from 339 students with 12 different parameters. The proposed algorithm is compared with a well-known logistic regression model. The accuracy of the proposed model before feature engineering and after feature engineering is better than logistic regression. The accuracy of the proposed model before feature engineering is 68.63%, while after feature engineering, it is 89.24%.

Keywords
Logistic Regression, Likert scale, Ordinal Data, Simple Linear Regression, p-value.

References
[1] Arundhathi Thangeda, Bakisanani Baratiseng, and Thatoyamodimo Mompati, "Education for Sustainability: Quality Education is a Necessity in Modern Day. How Far Do the Educational Institutions Facilitate Quality Education?," Journal of Education and Practice, vol. 7, no. 2, pp. 9-17, 2016.
[Google Scholar] [Publisher Link]
[2] Albert Victor Kelly, The Curriculum: Theory and Practice, 6th ed., SAGE Publications, pp. 1-336, 2009.
[Google Scholar] [Publisher Link]
[3] Burcu Tezcan-Unal, Kalman Winston, and Anne Qualter, "Learning-Oriented Quality Assurance in Higher Education Institutions," Quality in Higher Education, vol. 24, no. 3, pp. 221-237, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Renée Stalmeijer et al., "Strengthening Internal Quality Assurance Processes: Facilitating Student Evaluation Committees to Contribute," Assessment & Evaluation in Higher Education, vol. 41, no. 1, pp. 53-66, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Alexander C. McCormick, Jillian Kinzie, and Robert M. Gonyea, Student Engagement: Bridging Research and Practice to Improve the Quality of Undergraduate Education, Higher Education: Handbook of Theory and Research, pp. 47-92, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[6] I. Elaine Allen, and Christopher A. Seaman, "Likert Scales and Data Analyses," Quality Progress, vol. 40, no. 7, pp. 64-65, 2007.
[Google Scholar] [Publisher Link]
[7] Harry N. Boone Jr, and Deborah A. Boone, "Analyzing Likert Data," Journal of Extension, vol. 50, no. 2, pp. 1-6, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Spencer E. Harpe, "How to Analyze Likert and Other Rating Scale Data," Currents in Pharmacy Teaching and Learning, vol. 7, no. 6, pp. 836-850, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Paulo Soares, and Carlos Daniel Paulino, "Incomplete Categorical Data Analysis: A Bayesian Perspective," Journal of Statistical Computation and Simulation, vol. 69, no. 2, pp. 157-170, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Peter E Rossi, Zvi Gilula, and Greg M. Allenby, "Overcoming Scale Usage Heterogeneity: A Bayesian Hierarchical Approach," Journal of the American Statistical Association, vol. 96, no. 453, pp. 20-31, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Shaun R. Seaman, and Sylvia Richardson, "Equivalence of Prospective and Retrospective Models in the Bayesian Analysis of Case-Control Studies," Biometrika, vol. 91, no. 1, pp. 15-25, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Samiran Sinha, Bhramar Mukherjee, and Malay Ghosh, "Bayesian Semiparametric Modeling for Matched Case-Control Studies with Multiple Disease States," Biometrics, vol. 60, no. 1, pp. 41-49, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Tom Leonard and Hsu, J. S. J., "The Bayesian Analysis of Categorical Data: A Selective Review," In P. R. Freeman & A. F. M. Smith (Eds.), Aspects of uncertainty: A tribute to D. V. Lindley, pp. 283-310, 1994.
[Google Scholar]
[14] Sander Greenland, "An Application of Logistic Models to the Analysis of Ordinal Responses," Biometrical, vol. 27, no. 2, pp. 189-197, 1985.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Werner Holtbrügge, and Martin Schumacher, "A Comparison of Regression Models for the Analysis of Ordered Categorical Data," Journal of the Royal Statistical Society: Applied Statistics, Series C, vol. 40, no. 2, pp. 249-259, 1991.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Cyrus R. Mehta, Nitin Patel, and Pralay Senchaudhuri, "Exact Stratified Linear Rank Tests for Ordered Categorical and Binary Data," Journal of Computational and Graphical Statistics, vol. 1, no. 1, pp. 21-40, 1992.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Cyrus R. Mehta, Nitin R. Patel, and Anastasios A. Tsiatis, "Exact Significance Testing to Establish Treatment Equivalence with Ordered Categorical Data," Biometrics, vol. 40, no. 3, pp. 819-825, 1984.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Mokhammad Ridwan Yudhanegara, "Carefully to Analyze the Data Type of Ordinal Scale, Why?," Proceedings the 4th SEA-DR 2016, pp. 215-221, 2016.
[Google Scholar] [Publisher Link]
[19] Teng Xiuyi, and Gong Yuxia, "Research on Application of Machine Learning in Data Mining," IOP Conference Series: Materials Science and Engineering, vol. 392, no. 6, pp. 1-4, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] S.B. Kotsiantis, Supervised Machine Learning: A Review of Classification Techniques, Emerging Artificial Intelligence Applications in Computer Engineering, IOS Press, vol. 160, no. 1, pp. 3-24, 2007.
[Google Scholar] [Publisher Link]
[21] Chang Zhenhai, and Liu Zuo, "Logistic Regression Model and its Application," Journal of Yanbian University (Natural Science), vol. 38, no. 1, pp. 28-32, 2012.
[Google Scholar] [Publisher Link]
[22] Michael P. LaValley, "Logistic Regression," Circulation, vol. 117, no. 18, pp. 2395-2399, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[23] David G. Kleinbaum, Mitchel Klein, and Erica Rihl Pryor, Logistic Regression: A Self-Learning Text, 2nd ed., Springer, pp. 1-513, 2002.
[Google Scholar] [Publisher Link]
[24] Xiaonan Zou et al., "Logistic Regression Model Optimization and Case Analysis," 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China, pp. 135-139, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Jing Ren et al., "A Potential Field Model Using Generalized Sigmoid Functions," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 2, pp. 477-484, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Keith A. Maril, "Advanced Statistics: Linear Regression, Part I: Simple Linear Regression," Academic Emergency Medicine, vol. 11, no. 1, pp. 87-93, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Mahmoud A. Mahmoud, J.P. Morgan, and William H. Woodall, "The Monitoring of Simple Linear Regression Profiles with Two Observations Per Sample," Journal of Applied Statistics, vol. 37, no. 8, pp. 1249-1263, 2010.
[CrossRef] [Google Scholar] [Publisher Link]