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Direct linkChen Zhan; Srecko Joksimovic; Djazia Ladjal; Thierry Rakotoarivelo; Ruth Marshall; Abelardo Pardo – IEEE Transactions on Learning Technologies, 2024
Data are fundamental to Learning Analytics (LA) research and practice. However, the ethical use of data, particularly in terms of respecting learners' privacy rights, is a potential barrier that could hinder the widespread adoption of LA in the education industry. Despite the policies and guidelines of privacy protection being available worldwide,…
Descriptors: Privacy, Learning Analytics, Ethics, Data Use
Deho, Oscar Blessed; Joksimovic, Srecko; Li, Jiuyong; Zhan, Chen; Liu, Jixue; Liu, Lin – IEEE Transactions on Learning Technologies, 2023
Many educational institutions are using predictive models to leverage actionable insights using student data and drive student success. A common task has been predicting students at risk of dropping out for the necessary interventions to be made. However, issues of discrimination by these predictive models based on protected attributes of students…
Descriptors: Learning Analytics, Models, Student Records, Prediction
Wang, Minjuan; Yu, Haiyang; Bell, Zerla; Chu, Xiaoyan – IEEE Transactions on Learning Technologies, 2022
The Metaverse is a network of 3-D virtual worlds supporting social connections among its users and enabling them to participate in activities mimicking real life. It merges physical and virtual reality and provides channels for multisensory interactions and immersions in a variety of environments (Mystakidis, 2022). The Metaverse is considered the…
Descriptors: Technology Uses in Education, Computer Simulation, Educational Environment, Best Practices
