Classification Approach for Evaluating Students Performance in Covid 19 Pandemic

Author:

Sawant Poonam1,Gupta Sachin2,Sharma Yogesh2,Singh Anamika2

Affiliation:

1. Sinhgad Institute of Management & Computer Application Pune, India

2. Sinhgad Institute of Management & Computer Application, Pune, Maharashtra , India

Abstract

Student performance evaluation and analysis is a necessary task for improving students’ quality now a days. The main aim of this research is to analyze students’ performance during Covid-19 pandemic. Covid-19 pandemic impact has been extensive, affecting the education sector in India as well as world. In attempt to reduce the spread of Covid-19 government decided to temporarily close educational institutions. In response to schools and colleges closures, UNESCO recommended the use of distance learning programmes and online platforms to reach learners remotely and limit the disruption of education. This impacts not only on students’ phycology, on their performance too. Although there are many systems that have been implemented predictive analytics till date, better advancements is needed. Machine learning classifiers and related technologies can be used efficiently in performance evaluation. At the end of this paper, we have proposed an Architecture of Student’s Performance Evaluation System with classification techniques.

Publisher

Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP

Subject

Computer Science Applications,General Engineering,Environmental Engineering

Reference11 articles.

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4. Classification Model of Prediction for Placement of Students, Ajay Kumar Pal ,S.Pal I.J.Modern Education and Computer Science, 2013, 11, 49-56 Published Online November 2013 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2013.11.07

5. Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification, Surjeet Kumar Yadav,S.Pal World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 2, No. 2, 51-56, 2012

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment;International Journal of Emerging Technologies in Learning (iJET);2021-12-21

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