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Opinion Prediction of Hungarian Students for Real-Time E-Learning Systems: A Futuristic Sustainable Technology-Based Solution

Author

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  • Chaman Verma

    (Department of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053 Budapest, Hungary)

  • Zoltán Illés

    (Department of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053 Budapest, Hungary)

  • Veronika Stoffová

    (Department of Mathematics and Computer Science, Faculty of Education, Trnava University, 91843 Trnava, Slovakia)

  • Viktória Bakonyi

    (Department of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053 Budapest, Hungary)

Abstract

This work is a new step towards the understanding of students’ opinions about the use of technology in learning and improvements to provide sustainable E-learning solutions. Every higher educational university tries to provide well-suited, updated, and trending technology-based education facilities to its students. The task of analyzing the student’s sentiment about technology delivers benefits not only to ICT administrators, but also to management to become aware of the technological concerns. The opinions of Hungarian university students were analyzed using the regression method. We investigated 165 primary samples supported by the four hypotheses. The reliability of the data sample was calculated as 0.91 with Cronbach alpha testing. The Pearson Momentum Correlation (PMC) proved that the suggested technology benefits had a linear positive association with the student’s opinion. Furthermore, technology usability was positively correlated with the benefits. The supporting results of the regression model evidenced the significant impact of technology usability and benefits on the opinions. Using Exploratory Factor Analysis (EFA), we proposed significant features for the model that predicted students’ opinions using the educational benefit and usability parameters. These parameters statistically significantly predicted student’s opinions: F (2, 162) = 104.9, p < 0.05, R 2 = 0.559. This study may be supportive of implementing the opinion mining model online and useful to university authorities to understand better the students’ sentiments about the current technological facilities provided. The authors proposed an opinion mining model to deploy on the university’s real-time “E-lection” sustainable technology.

Suggested Citation

  • Chaman Verma & Zoltán Illés & Veronika Stoffová & Viktória Bakonyi, 2020. "Opinion Prediction of Hungarian Students for Real-Time E-Learning Systems: A Futuristic Sustainable Technology-Based Solution," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6321-:d:395122
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    References listed on IDEAS

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    1. Yanguang Chen, 2016. "Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-19, January.
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