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Yacobson, Elad; Fuhrman, Orly; Hershkowitz, Sara; Alexandron, Giora – Journal of Learning Analytics, 2021
Learning analytics have the potential to improve teaching and learning in K-12 education, but as student data is increasingly being collected and transferred for the purpose of analysis, it is important to take measures that will protect student privacy. A common approach to achieve this goal is the de-identification of the data, meaning the…
Descriptors: Identification, Privacy, Field Trips, Learning Analytics
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Khalila, Mohammad; Ebner, Martin – Journal of Learning Analytics, 2016
Learning analytics has reserved its position as an important field in the educational sector. However, the large-scale collection, processing, and analyzing of data has steered the wheel beyond the borders to face an abundance of ethical breaches and constraints. Revealing learners' personal information and attitudes, as well as their activities,…
Descriptors: Educational Research, Data Collection, Data Analysis, Technology Uses in Education
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Sclater, Niall – Journal of Learning Analytics, 2016
Ethical and legal objections to learning analytics are barriers to development of the field, thus potentially denying students the benefits of predictive analytics and adaptive learning. Jisc, a charitable organization that champions the use of digital technologies in UK education and research, has attempted to address this with the development of…
Descriptors: Data Analysis, Information Policy, Ethics, Standard Setting
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Prinsloo, Paul; Slade, Sharon – Journal of Learning Analytics, 2016
In light of increasing concerns about surveillance, higher education institutions (HEIs) cannot afford a simple paternalistic approach to student data. Very few HEIs have regulatory frameworks in place and/or share information with students regarding the scope of data that may be collected, analyzed, used, and shared. It is clear from literature…
Descriptors: Data Collection, Data Analysis, Educational Research, Information Security