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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Bibliographic Details
Published in:Informatics in education Vol. 22; no. 3; pp. 351 - 368
Main Author: Charytanowicz, Małgorzata
Format: Journal Article
Language:English
Published: Vilnius Vilniaus Universiteto Leidykla 01.09.2023
Vilnius University Press
Institute of Mathematics and Informatics
Vilnius University
Subjects:
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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Summary: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.
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ISSN:1648-5831
2335-8971
DOI:10.15388/infedu.2023.23