A Data-Driven Approach for Educational Improvement and Quality Enhancement

Anam Noor; Hafiz Muhammad Bilal

doi:10.64060/IJDSS.v1i1.2

Authors

Anam Noor Department of Computer Science, University of Agriculture Faisalabad, Punjab, Pakistan Author
Hafiz Muhammad Bilal Department of Computer and Software Engineering, Information Technology University, Lahore, Punjab, Pakistan Author

DOI:

https://doi.org/10.64060/IJDSS.v1i1.2

Keywords:

Predictive Analysis, Sentiment Analysis, Machine Learning, Educational Improvement

Abstract

Despite vast data availability, many organizations struggle to extract actionable insights, often lacking the necessary analytical tools. Sentiment analysis, or opinion mining, has proven effective for understanding user behavior and forecasting trends, particularly in social media. Building on previous research, this study applies sentiment analysis techniques to Quality Enhancement Cell (QEC) data to evaluate teachers' performance and gain insights into student perceptions. The proposed method uses machine learning algorithms to analyze sentiment and predictive patterns, offering objective support for teacher evaluation and fostering informed recommendations for educational development. The results emphasize data-driven improvements in teaching quality, highlighting sentiment analysis as a valuable tool for advancing educational outcomes based on stakeholder feedback.

Downloads

Download data is not yet available.

References

Afzal, J., Ahmad, S., & Aslam, N. (2016). An overview of internet of things. 8th South Asian International Conference,

Afzal, J., & Anwar, G. (2023). An empirical study on academic sustainability of mobile learning at university level. Pakistan journal of educational research, 6(2).

Alslaity, A., & Orji, R. (2024). Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions. Behaviour & Information Technology, 43(1), 139-164.

Bakar, S. (2021). Investigating the dynamics of contemporary pedagogical approaches in higher education through innovations, challenges, and paradigm shifts. Social Science Chronicle, 1(1), 1-19.

Batool, Z., & Qureshi, R. H. (2007). Quality assurance manual for higher education in Pakistan. Higher Education Commission, Pakistan.

Bettahi, A., Belouadha, F. Z., & Harroud, H. (2025). AI and EDM: Revolutionizing Global Education and Crafting a Personalized Digitally Advanced Learning. In Internationalization of Higher Education and Digital Transformation: Insights from Morocco and Beyond (pp. 243-258). Springer.

Durner, D., Leis, V., & Neumann, T. (2021). JSON tiles: Fast analytics on semi-structured data. Proceedings of the 2021 International Conference on Management of Data,

Fatima, A., Amjad, B., & Afzal, J. (2025). Statistical analysis to assess the satisfaction level of foreign students in China concerning accommodation provided by the university. SCOPUA Journal of Applied Statistical Research, 1(1).

Gharehchopogh, F. S., & Khalifelu, Z. A. (2011). Analysis and evaluation of unstructured data: text mining versus natural language processing. 2011 5th International Conference on Application of Information and Communication Technologies (AICT),

Kaliraj, P., Singaravelu, G., & Devi, T. (2024). Transformative digital technology for disruptive teaching and learning. CRC Press.

Kesavaraj, G., & Sukumaran, S. (2013). A study on classification techniques in data mining. 2013 fourth international conference on computing, communications and networking technologies (ICCCNT),

Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and information technologies, 26(1), 205-240.

Khatoon, H. (2025). The Impact of Digital Devices on the Study Habits and Academic Performance of Students. https://doi.org/10.64060/978-627-7898-03-8

Mishra, S., & Misra, A. (2017). Structured and unstructured big data analytics. 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC),

Rekha, K., Gopal, K., Satheeskumar, D., Anand, U. A., Doss, D. S. S., & Elayaperumal, S. (2024). Ai-Powered Personalized Learning System Design: Student Engagement and Performance Tracking System. 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE),

Rothwell, W. J., Zaballero, A., & Sadique, F. (2024). Measuring the Return on Investment (ROI) in Technology-Based Learning. In Trends and Issues in Instructional Design and Technology (pp. 237-251). Routledge.

Sackett, P. R., & Kuncel, N. R. (2018). Eight myths about standardized admissions testing. Measuring success: Testing, grades, and the future of college admissions, 13-39.

Sowndarya, C., Dahiya, S., Arora, A., Bhardwaj, A., Kumar, M., Ray, M., & Ramasubramanian, V. (2024). Comparative Analysis of Machine Learning and Deep Learning Models for Aspect-based Sentiment Analysis in Education. Journal of Scientific Research and Reports, 30(12), 567-576.

Wang, Y., Sun, C., & Guo, Y. (2021). A multi-attribute fuzzy evaluation model for the teaching quality of physical education in colleges and its implementation strategies. International Journal of Emerging Technologies in Learning (iJET), 16(2), 159-172.

Wilson, A., & Anwar, M. R. (2024). The Future of Adaptive Machine Learning Algorithms in High-Dimensional Data Processing. International Transactions on Artificial Intelligence, 3(1), 97-107.

Zhang, H., Xiao, B., Li, J., & Hou, M. (2021). An Improved Genetic Algorithm and Neural Network‐Based Evaluation Model of Classroom Teaching Quality in Colleges and Universities. Wireless Communications and Mobile Computing, 2021(1), 2602385.

Zhang, J., & Qu, S. (2021). Optimization of backpropagation neural network under the adaptive genetic algorithm. Complexity, 2021(1), 1718234.