Case Study: Postgraduate Students’ Class Engagement in Various Online Learning Contexts When Taking Privacy Issues to Incorporate with Artificial Intelligence Applications
The University of Hong Kong
How they used Delve
Researchers at the University of Hong Kong used Delve to deductively code nine Zoom interviews with postgraduate students against the four factors of the Online Student Engagement scale, studying whether AI privacy consent changes how engaged students feel in online classes.
“The researchers converted the data into transcripts, coded, and categorized them into interpretable data under the OSE framework via an online qualitative research software called Delve Tool. Deductive codes were derived from the theoretical framework of OSE and relevant kinds of literature related to the 4 OSE factors. Codes extracted from the data were then reviewed to identify, categorized, and rearranged based on the categories using Delve Tool.”
- Field
- Educational technology / AI and data privacy in online learning
- Data
- Nine individual Zoom interviews with postgraduate students randomly chosen from the two experimental groups of a 99-student quasi-experiment on AI applications and data privacy consent in online learning
- Approach
- Qualitative arm of a mixed-methods project whose first phase was quasi-experimental; deductive codes derived from the Online Student Engagement (OSE) framework and its literature, applied to transcripts, then reviewed, categorised and rearranged into the four OSE factors of skills, emotion, participation and performance
- Data types
- Interviews
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.
Citation
Fang Cheng, Alex Wing Cheung Tse (2023). 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. https://doi.org/10.18178/ijlt.9.2.90-95