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A Comparative Study of Students Online Learning During Pandemic Using Machine Learning Model
4th International Conference on Communications and Cyber-Physical Engineering, ICCCE 2021 ; 828:17-27, 2022.
Article in English | Scopus | ID: covidwho-1877772
ABSTRACT
As we know that COVID-19 Pandemic persists and it is resilient for students to attend classroom due to health concerns. Almost every academic institution has shifted towards an online education system. It is mandatory to encounter problems which are usually faced by students during online education. A survey was conducted to spot whether a student is contented with this new era of online education or not, by foreseeing the complications and outcomes of online education. A questionnaire with variegated sections was shared among students to cover supplementary issues faced by them and a response about the same was received from 263 students in total. Proposed model of classification will acquire data for training from the survey. It will predict the sentiment of students towards online education. Along with the model, the survey data is useful to discover additional problems faced by students and also do the needful in favor of students. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th International Conference on Communications and Cyber-Physical Engineering, ICCCE 2021 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th International Conference on Communications and Cyber-Physical Engineering, ICCCE 2021 Year: 2022 Document Type: Article