TY - JOUR
T1 - Ethics and educational technologies
AU - Parsons, Thomas D.
N1 - Publisher Copyright:
© 2020, Association for Educational Communications and Technology.
PY - 2021/2
Y1 - 2021/2
N2 - This paper is in response to the manuscript entitled “Ethical oversight of student data in learning analytics: a typology derived from a cross-continental, cross-institutional perspective” (Willis et al., Educ Technol Res Dev 64(5):881–901, 2016) from an ethical perspective. The impact of the article is that it offered a working typology of ethical approaches and aims to determine the ethical intersection of internal student data usage and application. Their review of ethical approaches included research institutions from three continents. Findings from this review have implications for applied research with student data. This is particularly interesting given the differences in ethical approaches in the United States (more utilitarian) and the European Union’s rather strict deontological approach. While learning analytics offer rich student data for personalizing educational technologies, there is associated potential for threats to autonomy and privacy. A limitation of the topology is that it did not include other people groups (e.g., Asia, South America, Australia). Future learning analytics, design, application, and research will need to consider both where the technologies were developed and where educational technologies are being applied.
AB - This paper is in response to the manuscript entitled “Ethical oversight of student data in learning analytics: a typology derived from a cross-continental, cross-institutional perspective” (Willis et al., Educ Technol Res Dev 64(5):881–901, 2016) from an ethical perspective. The impact of the article is that it offered a working typology of ethical approaches and aims to determine the ethical intersection of internal student data usage and application. Their review of ethical approaches included research institutions from three continents. Findings from this review have implications for applied research with student data. This is particularly interesting given the differences in ethical approaches in the United States (more utilitarian) and the European Union’s rather strict deontological approach. While learning analytics offer rich student data for personalizing educational technologies, there is associated potential for threats to autonomy and privacy. A limitation of the topology is that it did not include other people groups (e.g., Asia, South America, Australia). Future learning analytics, design, application, and research will need to consider both where the technologies were developed and where educational technologies are being applied.
KW - Algorithmic devices
KW - Educational technology
KW - Ethics
KW - Extended cognition
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U2 - 10.1007/s11423-020-09846-6
DO - 10.1007/s11423-020-09846-6
M3 - Article
AN - SCOPUS:85092926708
SN - 1042-1629
VL - 69
SP - 335
EP - 338
JO - Educational Technology Research and Development
JF - Educational Technology Research and Development
IS - 1
ER -