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.

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