| Field |
Value |
Language |
|
dc.contributor.author |
Wise, AF |
|
|
dc.contributor.author |
Knight, S
https://orcid.org/0000-0002-8709-5780
|
|
|
dc.contributor.author |
Ochoa, X |
|
|
dc.date.accessioned |
2021-12-17T01:30:11Z |
|
|
dc.date.available |
2021-12-17T01:30:11Z |
|
|
dc.identifier.citation |
Journal of Learning Analytics, 8, (3), pp. 1-9 |
|
|
dc.identifier.issn |
1929-7750 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/152382
|
|
|
dc.description.abstract |
<jats:p>The ongoing changes and challenges brought on by the COVID-19 pandemic have exacerbated long-standing inequities in education, leading many to question basic assumptions about how learning can best benefit all students. Thirst for data about learning is at an all-time high, sometimes without commensurate attention to ensuring principles this community has long valued: privacy, transparency, openness, accountability, and fairness. How we navigate this dynamic context is critical for the future of learning analytics. Thinking about the issue through the lens of JLA publications over the last eight years, we highlight the important contributions of “problem-centric” rather than “tool-centric” research. We also value attention (proximal or distal) to the eventual goal of closing the loop, connecting the results of our analyses back to improve the learning from which they were drawn. Finally, we recognize the power of cycles of maturation: using information generated about real-world uses and impacts of a learning analytics tool to guide new iterations of data, analysis, and intervention design. A critical element of context for such work is that the learning problems we identify and choose to work on are never blank slates; they embed societal structures, reflect the influence of past technologies; and have previous enablers, barriers and social mediation acting on them. In that context, we must ask the hard questions: What parts of existing systems is our work challenging? What parts is it reinforcing? Do these effects, intentional or not, align with our values and beliefs? In the end what makes learning analytics matter is our ability to contribute to progress on both immediate and long-standing challenges in learning, not only improving current systems, but also considering alternatives for what is and what could be. This requires including stakeholder voices in tackling important problems of learning with rigorous analytic approaches to promote equitable learning across contexts. This journal provides a central space for the discussion of such issues, acting as a venue for the whole community to share research, practice, data and tools across the learning analytics cycle in pursuit of these goals.</jats:p> |
|
|
dc.language |
en |
|
|
dc.publisher |
Society for Learning Analytics Research |
|
|
dc.relation.ispartof |
Journal of Learning Analytics |
|
|
dc.relation.isbasedon |
10.18608/jla.2021.7647 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.subject |
1303 Specialist Studies in Education |
|
|
dc.title |
What Makes Learning Analytics Research Matter |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
8 |
|
|
utslib.for |
1303 Specialist Studies in Education |
|
|
pubs.organisational-group |
/University of Technology Sydney |
|
|
pubs.organisational-group |
/University of Technology Sydney/Provost |
|
|
pubs.organisational-group |
/University of Technology Sydney/Provost/TD School |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.date.updated |
2021-12-17T01:30:10Z |
|
|
pubs.issue |
3 |
|
|
pubs.publication-status |
Published online |
|
|
pubs.volume |
8 |
|
|
utslib.citation.issue |
3 |
|
