Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{196620,
        author = {Swapnil Darji and Ashwini Gore and Dr. Seema Chowhan and Ms. Chetana. G. Patil},
        title = {Assessing The Impact Of Open Elective Implementation Under NEP 2020 Using Student Feedback And Machine Learning Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3748-3754},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196620},
        abstract = {Multidisciplinary education through Open Elective (OE) courses is one of the major guidelines for National Education Policy (NEP) 2020 for higher educational institutions. The assessment of such a huge amount of data from the input of all stakeholders is challenging task. A survey has been conducted from all the stakeholders, i.e., students, teachers and administration for their opinion about implementing open elective in their curriculum according to NEP 2020 primary data method was used. The survey shall utilize the   structured questionnaire based on a five-point Likert scale to compile participants’ perceptions, satisfaction levels, and opinions about the effectiveness and implementation of the Open Elective system. This questionnaire is designed to cover key factors such as Freedom of choosing OE Subject, Quality of teaching, how transparent the evaluation system is, Consistency of subjects, etc. The purpose of this research is to provide objective evaluation of the OE system through the use of artificial intelligence and machine learning technologies. Multiple machine learning algorithms including Random Forest, Gradient Boosting, AdaBoost algorithms, were utilized to analyse the collected data, which categorized this data points into three levels based on stakeholders’ satisfaction like: Low, Medium and High. The developed AI-based classification model demonstrates high predictive accuracy in determining student satisfaction levels regarding the Open Elective (OE) program. The results prove the efficiency of the machine learning algorithms in analysing the feedback data of the students in the higher educational system. From the feature importance analysis, it is clear, that teaching quality, learning outcomes, and curriculum usefulness are the major factors that influences overall student satisfaction. These factors significantly shape students’ perceptions of the effectiveness and academic value of the OE framework.},
        keywords = {Artificial Intelligence, Classification Models, Machine Learning, Student Satisfaction Prediction.},
        month = {April},
        }