Students' feedback- An effective tool towards enhancing the Teaching Learning Process

Authors

DOI:

https://doi.org/10.4108/eetsis.3347

Keywords:

students feedback, teachers' evaluation, teaching learning process, Quality

Abstract

INTRODUCTION: Evaluation is a key activity to improve the quality of service in every domain. However, it becomes quite challenging to measure the performance in some areas. Teaching is one such domain where it is bit intricating to evaluate the performance of teaching community. Here, in this work, authors have proposed effective usage of students’ feedback to enhance the quality of teaching learning process.

OBJECTIVES: The objective of this paper is to scientific and well-defined approach for teacher’s performance evaluation. This can help the faculty to identify the strengths and weaknesses of their teaching and evaluation methods.

METHODS: Data analysis and data visualization techniques have been used gain useful insights of the stduents’ feedback on various parameters. In order to carry out the simulation, authors have considered the teaching learning process in an engineering college.

RESULTS: It is evident from the results obtained 12 that more than 50% agree that the feedback system is fare and beneficial for the quality improvement in teachers.

CONCLUSION: Such analysis not only provides the useful insights regarding avenue for im-provement, but also helps the management for appraisals to outperforming teachers.

References

J Moskowitz, C.S., Welch, M.L., Jacobs, M.A., Kurland, B.F. and Simpson, A.L., 2022. Ra-diomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies. Radiology, 2022, 304(2), 265-273.

N. Sultana and N. Sharma, "Statistical Models for Predicting Swine F1u Incidences in India," First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018, pp. 134-138, doi: 10.1109/ICSCCC.2018.8703300.

S. Verma and N. Sharma, "Statistical Models for Predicting Chikungunya Incidences in India," First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018, pp. 139-142, doi: 10.1109/ICSCCC.2018.8703218.

Zhang, Q.T., Wong, K.M., Yip, P.C. and Reilly, J.P., Statistical analysis of the performance of information theoretic criteria in the detection of the number of signals in array processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(10), 1557-1567.

Roddy, A.R. and Stosz, J.D.,. Fingerprint features-statistical analysis and system performance estimates. Proceedings of the IEEE, 1997, 85(9), pp.1390-1421.

Sharma, N., XGBoost. The extreme gradient boosting for mining applications. 2018, GRIN Verlag.

Priyavrat and N. Sharma, "Sentiment Analysis using tidytext package in R," First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018, pp. 577-580, doi: 10.1109/ICSCCC.2018.8703296.

Callahan, S.P., Freire, J., Santos, E., Scheidegger, C.E., Silva, C.T. and Vo, H.T., June. VisTrails: visualization meets data management. In Proceedings of the ACM SIGMOD international conference on Management of data , 2006, pp. 745-747.

Meyer, R.D. and Cook, D., Visualization of data. Current Opinion in Biotechnology, 2000, 11(1), 89-96.

Chen, C.H., Härdle, W.K. and Unwin, A. eds. Handbook of data visualization. Springer Science & Business Media, 2007.

Sadiku, M., Shadare, A.E., Musa, S.M., Akujuobi, C.M. and Perry, R. Data visualization. International Journal of Engineering Research And Advanced Technology (IJERAT), 2016, 2(12), pp.11-16.

Misiejuk, K., Wasson, B., Egelandsdal, K. Using learning analytics to understand student perceptions of peer feedback. Computers in Human Behavior , 2021, 117, 2021.

Sun, X., Cai, C., Pan, S., Bao, n., Liu, N. A University Teachers’ Teaching Performance Evaluation Method Based on Type-II Fuzzy Sets. Mathematics, 2021, 9, 2126.

Ifeanyi, G., Ndukwe, Ben, K., Daniel D., and Russell, J. Data Science Approach for Simulating Educational Data: Towards the Development of Teaching Outcome Model (TOM). Big Data and Cognitive Computing, 2018, 2,24.

Samant, P., Khunger, A., Arya, R., Islam, M., Kanupriya, Bhushan, M. Latest Tools for Data Mining and Machine Learning. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2019, 9(8), pp. 18-23.

U. Verma, C. Garg, M. Bhushan, P. Samant, A. Kumar, A. Negi, "Prediction of students’ academic performance using Machine Learning Techniques," 2022 International Mobile and Embedded Technology Conference (MECON), IEEE, 10-11 March, 2022, pp. 151-156, doi: 10.1109/MECON53876.2022.9751956.

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Published

03-07-2023

How to Cite

1.
Mangla M, Mehta V, Pattnaik CR, Mohanty SN. Students’ feedback- An effective tool towards enhancing the Teaching Learning Process . EAI Endorsed Scal Inf Syst [Internet]. 2023 Jul. 3 [cited 2024 Jul. 22];10(5). Available from: https://publications.eai.eu/index.php/sis/article/view/3347

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