AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms

Authors

  • J. Prakash Hindusthan Institute of Technology, Coimbatore
  • R. Swathiramya Department of AIDS, SNS College of Engineering,Coimbatore
  • G. Balambigai Assistant Professor, Department of EEE, Akshaya College of Engineering and Technology, Coimbatore.
  • R. Menaha Department of Information Technology, Sri Eshwar College of ENGINEERING, Coimbatore
  • J.S. Abhirami Department of Artificial Intelligence and Data Science, Nehru Institute of engineering and technology, Coimbatore

DOI:

https://doi.org/10.22399/ijcesen.780

Keywords:

AI-driven feedback, Machine learning algorithms, Personalized learning, Emotional intelligence, Decision trees, Deep learning

Abstract

The rapid evolution of educational technologies has led to a shift toward personalized and adaptive learning experiences. A critical component of such systems is the ability to provide timely and relevant feedback to students. This paper presents an AI-driven real-time feedback system designed to enhance student support through the integration of sentiment analysis and machine learning algorithms. The system leverages sentiment analysis to gauge the emotional tone of student interactions, such as forum posts, assignment submissions, and feedback. Machine learning algorithms, including decision trees, support vector machines (SVM), and deep learning models, are used to analyze and predict student engagement, performance, and emotional states. By combining both cognitive and emotional insights, the system delivers personalized, context-sensitive feedback that helps students overcome learning challenges and improve academic outcomes. The effectiveness of the system is evaluated using multiple datasets, showing significant improvements in student engagement, satisfaction, and performance.

References

Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. doi: 10.1561/1500000011.

D'Mello, S. K., & Graesser, A. C. (2015). Sentiment Analysis in Education. Handbook of Research on Learning Analytics, 417-428. doi: 10.1561/1500000011. DOI: https://doi.org/10.1561/1500000011

Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618. DOI:10.1109/TSMCC.2010.2053532 DOI: https://doi.org/10.1109/TSMCC.2010.2053532

Shute, V. J., & Kim, Y. J. (2014). Formative and Summative Uses of Game-Based Assessment. Yearbook of the National Society for the Study of Education, 113(2), 275-303. DOI:10.1007/978-1-4614-3185-5_25 DOI: https://doi.org/10.1007/978-1-4614-3185-5_25

Pekrun, R., & Linnenbrink-Garcia, L. (2012). Emotion and achievement during adolescence. Educational Psychologist, 47(1), 21-31. https://doi.org/10.1111/cdep.12237 DOI: https://doi.org/10.1111/cdep.12237

Anderson, C. A., & Dill, K. E. (2000). Video Games and Aggressive Thoughts, Feelings, and Behavior in the Laboratory and in Life. Journal of Personality and Social Psychology, 78(4), 772-790. https://doi.org/10.1037/0022-3514.78.4.772 DOI: https://doi.org/10.1037//0022-3514.78.4.772

Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851 DOI: https://doi.org/10.1177/0002764213498851

K. Kalpana., Nikhil Sankar. (2015). New Secure and Advanced Algorithm for Stream Ciphers Extended RC4 and FPGA Implementation International Journal of Science and Research (IJSR), 4(3);2044-2048

Kim, D., & Yang, H. (2017). Integrating Sentiment Analysis with Learning Analytics to Predict Student Success in Online Learning Environments. Educational Technology Research and Development, 65(6), 1215-1230.

Nicol, D., & Macfarlane-Dick, D. (2020). Formative Assessment and Feedback: A Review. Studies in Higher Education, 45(5), 1027-1038. https://doi.org/10.1080/03075070600572090 DOI: https://doi.org/10.1080/03075070600572090

Liu, B., & Zhang, L. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167. DOI: https://doi.org/10.2200/S00416ED1V01Y201204HLT016

Huang, L., Sun, Z., & Chen, H. (2015). Emotional analysis in student feedback: A case study of sentiment mining. International Journal of Educational Technology in Higher Education, 12(2), 25-38.

Menaha, R., & Ananthi, K. (2024). Reviewing the effectiveness of lexicon-based techniques for sentiment analysis in massive open online courses. International Journal of Data Science and Analytics, 1-12 https://doi.org/10.1007/s41060-024-00585-y DOI: https://doi.org/10.1007/s41060-024-00585-y

Menaha, R., Dhanaranjani, R., Rajalakshmi, T., & Yogarubini, R. (2017). Student feedback mining system using sentiment analysis. IJCATR, 6, 1-69. DOI:10.7753/IJCATR0601.1009 DOI: https://doi.org/10.7753/IJCATR0601.1009

Zhang, Y., & Zhao, Y. (2017). Predicting academic success using deep learning. Journal of Educational Data Mining, 9(2), 1-19.

Chitkasaem Suwanrath, Ninlapa Pruksanusak, Ounjai Kor-anantakul, Thitima Suntharasaj, Tharangrut Hanprasertpong and Savitree Pranpanus. (2013). Reliability of fetal nasal bone length measurement at 11–14 weeks of gestation, BMC Pregnancy and Childbirth, 13:7. https://doi.org/10.1186/1471-2393-13-7 DOI: https://doi.org/10.1186/1471-2393-13-7

Cui, Chen; Dong, Fajin (2022), Dataset for Fetus Framework, Mendeley Data, V1. DOI:10.17632/n2rbrb9t4f.1

Mendeley dataset: https://data.mendeley.com/datasets/n2rbrb9t4f/1

FMF dataset: https://fetalmedicine.org/education/fetal-abnormalities.

Sasikala, S., Sasipriya, S., & Maheshwari, U. (2022). Soft Computing based Brain Tumor Categorization with Machine Learning Techniques. In 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA) (pp. 1-9). DOI:10.1109/ICACTA54488.2022.9752880 DOI: https://doi.org/10.1109/ICACTA54488.2022.9752880

S. Praseetha, & S. Sasipriya. (2024). Adaptive Dual-Layer Resource Allocation for Maximizing Spectral Efficiency in 5G Using Hybrid NOMA-RSMA Techniques. International Journal of Computational and Experimental Science and Engineering, 10(4);1130-1139. https://doi.org/10.22399/ijcesen.665 DOI: https://doi.org/10.22399/ijcesen.665

P., A. M., & R. GUNASUNDARI. (2024). An Interpretable PyCaret Approach for Alzheimer’s Disease Prediction. International Journal of Computational and Experimental Science and Engineering, 10(4);1223-1232. https://doi.org/10.22399/ijcesen.655 DOI: https://doi.org/10.22399/ijcesen.655

P. Padma, & G. Siva Nageswara Rao. (2024). CBDC-Net: Recurrent Bidirectional LSTM Neural Networks Based Cyberbullying Detection with Synonym-Level N-Gram and TSR-SCSOFeatures. International Journal of Computational and Experimental Science and Engineering, 10(4);1486-1500. https://doi.org/10.22399/ijcesen.623 DOI: https://doi.org/10.22399/ijcesen.623

M, P., B, J., B, B., G, S., & S, P. (2024). Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks. International Journal of Computational and Experimental Science and Engineering, 10(4);585-591. https://doi.org/10.22399/ijcesen.480 DOI: https://doi.org/10.22399/ijcesen.480

R. Deepa, V. Jayalakshmi, K. Karpagalakshmi, S. Manikanda Prabhu, & P.Thilakavathy. (2024). Survey on Resume Parsing Models for JOBCONNECT+: Enhancing Recruitment Efficiency using Natural language processing and Machine Learning. International Journal of Computational and Experimental Science and Engineering, 10(4);1394-1403. https://doi.org/10.22399/ijcesen.660 DOI: https://doi.org/10.22399/ijcesen.660

Nuthakki, praveena, & Pavankumar T. (2024). Comparative Assessment of Machine Learning Algorithms for Effective Diabetes Prediction and Care. International Journal of Computational and Experimental Science and Engineering, 10(4);1337-1343. https://doi.org/10.22399/ijcesen.606 DOI: https://doi.org/10.22399/ijcesen.606

LAVUDIYA, N. S., & C.V.P.R Prasad. (2024). Enhancing Ophthalmological Diagnoses: An Adaptive Ensemble Learning Approach Using Fundus and OCT Imaging. International Journal of Computational and Experimental Science and Engineering, 10(4);1541-1550. https://doi.org/10.22399/ijcesen.678

S, P. S., N. R., W. B., R, R. K., & S, K. (2024). Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3);341-349. https://doi.org/10.22399/ijcesen.395 DOI: https://doi.org/10.22399/ijcesen.395

Naresh Babu KOSURI, & Suneetha MANNE. (2024). Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios. International Journal of Computational and Experimental Science and Engineering, 10(4);1015-1031. https://doi.org/10.22399/ijcesen.560 DOI: https://doi.org/10.22399/ijcesen.560

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Published

2024-12-21

How to Cite

J. Prakash, R. Swathiramya, G. Balambigai, R. Menaha, & J.S. Abhirami. (2024). AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.780

Issue

Vol. 10 No. 4 (2024): IJCESEN

Section

Research Article

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