Online Education vs Traditional Education: Analysis of Student Performance in Computer Science using Shapley Additive Explanations

Nowadays, the rapid development of ICT has brought more flexible forms that push the boundaries of classic teaching methodology. This paper is an analysis of online teaching and learning forced by the COVID-19 pandemic, as compared with traditional education approaches. In this regard, we assessed t...

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Vydáno v:	Informatics in education Ročník 22; číslo 3; s. 351 - 368
Hlavní autor:	Charytanowicz, Małgorzata
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Vilnius Vilniaus Universiteto Leidykla 01.09.2023 Vilnius University Press Institute of Mathematics and Informatics Vilnius University
Témata:	Academic achievement Additives Algorithms Artificial Intelligence Averages CAI College Science Colleges & universities Communications technology Computer assisted instruction Computer science COVID-19 Distance Education Distance learning Distance learning / e-learning Education Educational Technology Electronic Learning Engineering Education Engineering Technology Higher Education Hybrid modes ICT Information and Communications Technologies Influence of Technology Information technology Instructional Materials Internet Learning Learning Motivation Machine learning Online Courses Online instruction online learning Pandemics Science and technology Scores SHAP values Student Motivation Student Participation Students Teaching Teaching methods Telecommunications Test scores Undergraduate Study XGBoost higher education COVID19 XGBoost SHAP values online learning student motivation
ISSN:	1648-5831, 2335-8971
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Shrnutí:	Nowadays, the rapid development of ICT has brought more flexible forms that push the boundaries of classic teaching methodology. This paper is an analysis of online teaching and learning forced by the COVID-19 pandemic, as compared with traditional education approaches. In this regard, we assessed the performance of students studying in the face-to-face, online and hybrid mode for an engineering degree in Computer Science at the Lublin University of Technology during the years 2019–2022. A total of 1827 final test scores were examined using machine learning models and the Shapley additive explanations method. The results show an average increase in performance on final tests scores for students using online and hybrid modes, but the difference did not exceed 10% of the point maximum. Moreover, the students’ work had a much higher impact on the final test scores than did the study system and their profile features.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1648-5831 2335-8971
DOI:	10.15388/infedu.2023.23