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General Information
ISSN:
2377-2891(Print); 2377-2905(Online)
Frequency:
Quarterly
Editor-in-Chief:
Prof. Xabier Basogain
Associate Executive Editor:
Ms. Jenny Jiang
DOI:
10.18178/ijlt
Abstracting/Indexing:
Google Scholar; Crossref, CNKI,
etc.
E-mail questions
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IJLT Editorial Office
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Editor-in-Chief
Prof. Xabier Basogain
University of the Basque Country, Vitoria-Gasteiz, Spain
I am very excited to serve as the first Editor-in-Chief of the International Journal of Learning and Teaching...[
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What's New
2023-09-05
Vol. 9, No. 3, September 2023 issue has been published!
2023-06-06
Vol. 9, No. 2, June 2023 issue has been published!
2023-03-09
Vol. 9, No. 1, March 2023 issue has been published!
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Volume 9, No. 2, June 2023
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Case Study: Postgraduate Students’ Class Engagement in Various Online Learning Contexts When Taking Privacy Issues to Incorporate with Artificial Intelligence Applications
Cheng Fang and Alex W. C. Tse*
The University of Hong Kong, Hong Kong SAR, China
*Correspondence: awctse@hku.hk (A.W.C.T.)
Abstract
—Artificial Intelligence (AI) has transformed the Education sector. It made it possible for academic institutions to personalize content according to students’ individual needs and improve administrative tasks such as grading assignments. This has increased efficiency in teaching and learning but has also raised relevant concerns about data privacy issues. Researchers have pointed out the potential hindrance of this concern to the further development and implementation of AI technology in Education. In this research project, the authors conducted a mixed method to investigate the above issue by assessing students’ class engagement in various online learning contexts when considering AI privacy issues or not. The first part of this project presented a quantitative approach (quasi-experimental design) while this paper focused on the qualitative approach (interviews) conducted with the same group of 99 students from the postgraduate school via Zoom. Individual student interviews were conducted with randomly chosen 9 students from the two experimental groups in phase one of this research, and thematic analysis was used to analyze the relevant data based on the 4-factor theoretical framework (skills, emotion, participation, and performance) from The Online Student Engagement Scale (OSE). The study discovered that the majority of the students regarded the privacy consent taken into consideration when implementing AI applications in an online learning context had enhanced their class engagement. In addition, the findings indicated that students’ emotions and participation engagements increased the most out of the four OSE factors.
Keywords
—artificial intelligence, applied computing, computing methodologies, security and privacy, science education, student class engagement
Cite: Cheng Fang and Alex W. C. Tse, "Case Study: Postgraduate Students’ Class Engagement in Various Online Learning Contexts When Taking Privacy Issues to Incorporate with Artificial Intelligence Applications," International Journal of Learning and Teaching, Vol. 9, No. 2, pp. 90-95, June 2023. doi: 10.18178/ijlt.9.2.90-95
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
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