ItemConference Paper

Using Machine Learning Sentiment Analysis to Evaluate Students Learning Impact

Title
Using Machine Learning Sentiment Analysis to Evaluate Students Learning Impact
Creator
Kumar, Santosh; Johri, Methily; Kumar, Kishan; Awasthi, Yashmita; Kakkar, Barkha; Nandan, Akash
Description
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.
Source
ICDT 2025 - 3rd International Conference on Disruptive Technologies;pp.21-24
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Adaptive Learning; Educational Data Mining; Learning Impact Evaluation; Machine Learning; Sentiment Analysis
Coverage
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 & Science, Uttar Pradesh, Ghaziabad, India; Nandan A., School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, Greater Noida, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151958-2;
Format
online
Language
English
Type
Conference paper

Citation

Kumar, Santosh; Johri, Methily; Kumar, Kishan; Awasthi, Yashmita; Kakkar, Barkha; Nandan, Akash, “Using Machine Learning Sentiment Analysis to Evaluate Students Learning Impact,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25975.

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