ERIC Number: EJ1421460
Record Type: Journal
Publication Date: 2024-Apr
Pages: 29
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1042-1726
EISSN: EISSN-1867-1233
Learning Analytics as Data Ecology: A Tentative Proposal
Paul Prinsloo; Mohammad Khalil; Sharon Slade
Journal of Computing in Higher Education, v36 n1 p154-182 2024
Central to the institutionalization of learning analytics is the need to understand and improve student learning. Frameworks guiding the implementation of learning analytics flow from and perpetuate specific understandings of learning. Crucially, they also provide insights into how learning analytics acknowledges and positions itself as entangled in institutional data ecosystems, and (increasingly) as part of a data ecology driven by a variety of data interests. The success of learning analytics should therefore be understood in terms of data flows and data interests informing the emerging and mutually constitutive interrelationships and interdependencies between different stakeholders, interests and power relations. This article analyses several selected frameworks to determine the extent to which learning analytics understands itself as a "data ecosystem" with dynamic interdependencies and interrelationships (human and non-human). Secondly, as learning analytics increasingly becomes part of broader "data ecologies," we examine the extent to which learning analytics takes cognizance of the reality, the potential and the risks of being part of a broader data ecology. Finally, this article examines the "different data interests" vested in learning analytics and critically considers implications for student data sovereignty. The research found that most of the analyzed frameworks understand learning analytics as a data ecosystem, with very little evidence of a broader data ecological understanding. The vast majority of analyzed frameworks consider student data as valuable resource without considering student data ownership and their data rights for self-determination.
Descriptors: Learning Analytics, Data, Ecology, Models, Interests, Data Use, Educational Improvement, Stakeholders, Power Structure, Risk
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A