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Autor/inn/enYacobson, Elad; Fuhrman, Orly; Hershkowitz, Sara; Alexandron, Giora
TitelDe-Identification Is Insufficient to Protect Student Privacy, or--What Can a Field Trip Reveal?
QuelleIn: Journal of Learning Analytics, 8 (2021) 2, S.83-92 (10 Seiten)Infoseite zur Zeitschrift
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ZusatzinformationORCID (Yacobson, Elad)
ORCID (Fuhrman, Orly)
ORCID (Alexandron, Giora)
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1929-7750
SchlagwörterIdentification; Privacy; Field Trips; Learning Analytics; Student Records; Data Collection; Foreign Countries; Elementary School Students; Secondary School Students; Israel
AbstractLearning 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 removal of personal details that can reveal student identity. However, as we demonstrate, de-identification alone is not a complete solution. We show how we can discover sensitive information about students by linking de-identified datasets with publicly available school data, using unsupervised machine learning techniques. This underlines that de-identification alone is insufficient if we wish to further learning analytics in K-12 without compromising student privacy. (As Provided).
AnmerkungenSociety for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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