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ERIC Number: EJ1421464
Record Type: Journal
Publication Date: 2024-Apr
Pages: 19
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1042-1726
EISSN: EISSN-1867-1233
Available Date: N/A
Using Networked Learning to Improve Learning Analytics Implementation
Stephanie J. Blackmon; Robert L. Moore
Journal of Computing in Higher Education, v36 n1 p183-201 2024
As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus' learning analytics practices than many colleges and universities currently provide. Therefore, in the current text, the authors leverage networked learning to create a networked learning analytics logic model that supports colleges and universities in developing more transparent, ethical, inclusive learning analytics plans. The authors build on their previous learning analytics framework as well as extant learning analytics literature to develop the networked learning analytics logic model. The model offers flexibility that allows for adaptive implementation by institutions that are both new to or already engaging in learning analytics initiatives. We encourage those considering learning analytics to implement the model and disseminate their findings so that the model can evolve to align with the dynamic nature of learning analytics implementations.
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link-springer-com.bibliotheek.ehb.be/
Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A