Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, Santosh; Johri, Methily; Kumar, Kishan; Awasthi, Yashmita; Kakkar, Barkha; Nandan, Akash Title A name given to the resource Using Machine Learning Sentiment Analysis to Evaluate Students Learning Impact Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived ICDT 2025 - 3rd International Conference on Disruptive Technologies;pp.21-24 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDT63985.2025.10986651" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDT63985.2025.10986651</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006466382?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006466382?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar S., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Johri M., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Kumar K., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India; Awasthi Y., School of Commerce, Finance and Account, Christ University, Karnataka, Bengaluru, India; Kakkar B., School of Management, Institute of Technology &amp; Science, Uttar Pradesh, Ghaziabad, India; Nandan A., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India Description An account of the resource For educational experiences and results to be improved, learning impact assessment is essential. Students' emotional reactions, which are crucial to their involvement and understanding, are frequently missed by traditional evaluation techniques. Through a review of student feedback, conversations, and course ratings, this study investigates the use of machine learning-based sentiment analysis to assess the impact of learning. Performance evaluations were conducted on a number of sentiment categorization models, including Nae Bayes, Support Vector Machines (SVM), Logistic Regression, Random Forest, Long Short-Term Memory (LSTM), and BERT. With an accuracy of 91.7%, the results show that BERT performs better than other models and offers more accurate sentiment classification. Accuracy and insights are further improved by combining textual, auditory, and visual signals in multi-modal sentiment analysis. The results show how sentiment analysis may be used to track feedback in real time facilitating adaptive learning techniques to raise student interest. Future studies should concentrate on expanding sentiment analysis applications to traditional and hybrid learning contexts, integrating multi-modal data, and ethical implications. 2025 IEEE. Subject The topic of the resource Adaptive Learning; Educational Data Mining; Learning Impact Evaluation; Machine Learning; Sentiment Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151958-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online