Advanced search
2 files | 1.04 MB Add to list

Datafication and algorithmization of education : how do parents and students evaluate the appropriateness of learning analytics?

Marijn Martens (UGent) , Ralf De Wolf (UGent) and Lieven De Marez (UGent)
Author
Organization
Project
Abstract
Algorithmic systems such as Learning Analytics (LA) are driving the datafication and algorithmization of education. In this research, we focus on the appropriateness of LA systems from the perspective of parents and students in secondary education. Anchored in the contextual integrity framework (Nissenbaum, Washington Law Review, 79, 41, 2004), we conducted two survey studies (N-students=277, N-parents=1013) in Flanders to investigate how they evaluate the appropriateness of the data flows in LA systems, and how both populations differ in their evaluations. The results show that the most-used student-centered LA are perceived less appropriate than the less-used teacher-centered LA by both students and parents. The usage of personal characteristics in LA is perceived as least appropriate, in contrast to coarser class characteristics. Sharing insights of LA with institutions that are part of the traditional educational context, such as the school, is seen as the most appropriate, and more appropriate than sharing it with learning platforms or third parties (e.g., Big Tech). Overall, we found that parents evaluated the different elements of the dataflows embedded in LA as less appropriate than students. In the discussion, we argue that educational institutions should include the evaluation of both parents and students to further manage expectations and construct shared norms and practices when implementing LA in education.
Keywords
Learning analytics, Appropriateness, Contextual integrity, Survey, Students, Parents, PRIVACY, PERCEPTIONS, CHALLENGES, TEACHER

Downloads

  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 872.61 KB
  • (...).docx
    • full text (Accepted manuscript)
    • |
    • UGent only (changes to open access on 2024-09-24)
    • |
    • ZIP archive
    • |
    • 164.78 KB

Citation

Please use this url to cite or link to this publication:

MLA
Martens, Marijn, et al. “Datafication and Algorithmization of Education : How Do Parents and Students Evaluate the Appropriateness of Learning Analytics?” EDUCATION AND INFORMATION TECHNOLOGIES, 2024, doi:10.1007/s10639-023-12124-6.
APA
Martens, M., De Wolf, R., & De Marez, L. (2024). Datafication and algorithmization of education : how do parents and students evaluate the appropriateness of learning analytics? EDUCATION AND INFORMATION TECHNOLOGIES. https://doi.org/10.1007/s10639-023-12124-6
Chicago author-date
Martens, Marijn, Ralf De Wolf, and Lieven De Marez. 2024. “Datafication and Algorithmization of Education : How Do Parents and Students Evaluate the Appropriateness of Learning Analytics?” EDUCATION AND INFORMATION TECHNOLOGIES. https://doi.org/10.1007/s10639-023-12124-6.
Chicago author-date (all authors)
Martens, Marijn, Ralf De Wolf, and Lieven De Marez. 2024. “Datafication and Algorithmization of Education : How Do Parents and Students Evaluate the Appropriateness of Learning Analytics?” EDUCATION AND INFORMATION TECHNOLOGIES. doi:10.1007/s10639-023-12124-6.
Vancouver
1.
Martens M, De Wolf R, De Marez L. Datafication and algorithmization of education : how do parents and students evaluate the appropriateness of learning analytics? EDUCATION AND INFORMATION TECHNOLOGIES. 2024;
IEEE
[1]
M. Martens, R. De Wolf, and L. De Marez, “Datafication and algorithmization of education : how do parents and students evaluate the appropriateness of learning analytics?,” EDUCATION AND INFORMATION TECHNOLOGIES, 2024.
@article{01HA9J0WZTRC4CXW150T36DPPV,
  abstract     = {{Algorithmic systems such as Learning Analytics (LA) are driving the datafication and algorithmization of education. In this research, we focus on the appropriateness of LA systems from the perspective of parents and students in secondary education. Anchored in the contextual integrity framework (Nissenbaum, Washington Law Review, 79, 41, 2004), we conducted two survey studies (N-students=277, N-parents=1013) in Flanders to investigate how they evaluate the appropriateness of the data flows in LA systems, and how both populations differ in their evaluations. The results show that the most-used student-centered LA are perceived less appropriate than the less-used teacher-centered LA by both students and parents. The usage of personal characteristics in LA is perceived as least appropriate, in contrast to coarser class characteristics. Sharing insights of LA with institutions that are part of the traditional educational context, such as the school, is seen as the most appropriate, and more appropriate than sharing it with learning platforms or third parties (e.g., Big Tech). Overall, we found that parents evaluated the different elements of the dataflows embedded in LA as less appropriate than students. In the discussion, we argue that educational institutions should include the evaluation of both parents and students to further manage expectations and construct shared norms and practices when implementing LA in education.}},
  articleno    = {{12124}},
  author       = {{Martens, Marijn and De Wolf, Ralf and De Marez, Lieven}},
  issn         = {{1360-2357}},
  journal      = {{EDUCATION AND INFORMATION TECHNOLOGIES}},
  keywords     = {{Learning analytics,Appropriateness,Contextual integrity,Survey,Students,Parents,PRIVACY,PERCEPTIONS,CHALLENGES,TEACHER}},
  language     = {{eng}},
  pages        = {{27}},
  title        = {{Datafication and algorithmization of education : how do parents and students evaluate the appropriateness of learning analytics?}},
  url          = {{http://doi.org/10.1007/s10639-023-12124-6}},
  year         = {{2024}},
}

Altmetric
View in Altmetric
Web of Science
Times cited: