Authors : Suhas Medam; Kethan Kiran Dosapati; Rajesh Boyana; Sanjay Kamatham; Anitha K

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/ymdd5tbm

DOI : https://doi.org/10.38124/ijisrt/24may601

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : "TeachMate" represents a transformative project in education, introducing a comprehensive platform that addresses critical challenges in assessment and learning. The system's four primary deliverables redefine the educational experience. Firstly, "Personalized Practice Material" leverages sophisticated algorithms to generate tailored questions and summaries, aligning with individual learning styles for heightened engagement and comprehension. Secondly, the "Efficient Automated Grading" feature employs advanced machine learning and natural language processing to streamline subjective answer evaluations, ensuring both accuracy and fairness while saving educators valuable time. Thirdly, "Data- driven Insights" empower educators with a deep understanding of student performance, facilitating targeted instructional strategies through analytics. Finally, the "Integrated Learning Environment" seamlessly combines these functionalities, creating a centralized hub where personalized practice material, automated grading, and insightful analytics converge. "TeachMate" envisions a holistic approach to education, where personalized learning tools, efficient assessment, and data-driven insights coalesce to enrich both the teaching and learning experience. TeachMate heralds a new era in education, seamlessly blending personalized learning, efficient assessment, and insightful analytics. It envisions a dynamic educational landscape where students receive tailored support, educators find unprecedented efficiency, and learning transcends traditional boundaries. In the convergence of these groundbreaking features, "TeachMate" stands as a beacon, illuminating a future where education is not just a process but a personalized, efficient, and insightful journey for every student and educator.

References :

1. J. Wang and Y. Dong, "Measurement of text similarity: A survey", Information, vol. 11, no. 9, pp. 421, Aug. 2020.
2. M. Han, X. Zhang, X. Yuan, J. Jiang, W. Yun and C. Gao, "A survey on the techniques applications and performance of short text semantic similarity", Concurrency Comput. Pract. Exper., vol. 33, no. 5, Mar. 2021.
3. M. S. M. Patil and M. S. Patil, "Evaluating Student descriptive answers using natural language processing", Int. J. Eng. Res. Technol., vol. 3, no. 3, pp. 1716-1718, 2014.
4. Niu n Xiao-Xiao and Y. Suen Ching, "A novel hybrid CNN-SVM classifier for recognizing handwritten digits", Elsevier, vol. 45, no. 4, pp. 1318-1325, April 2012.
5. Ma Caiyun and Zhang Hong, "Effective Handwritten Digit Recognition Based on Multi-feature Extraction and Deep Analysis", 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 297-301, 2015.
6. Ma Caiyun and Zhang Hong, "Effective Handwritten Digit Recognition Based on Multi-feature Extraction and Deep Analysis", 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 297-301, 2015.