Abstract
Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.
Original language | English |
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Title of host publication | LAK 2014 |
Subtitle of host publication | Fourth International Conference on Learning Analytics and Knowledge |
Editors | Stephanie Teasley, Abelardo Pardo |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 287-288 |
Number of pages | 2 |
ISBN (Print) | 9781595930361, 9781450326643 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | International Learning Analytics & Knowledge Conference 2014 - Indianapolis, United States of America Duration: 24 Mar 2014 → 28 Mar 2014 Conference number: 4th https://lak14indy.wordpress.com/ |
Conference
Conference | International Learning Analytics & Knowledge Conference 2014 |
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Abbreviated title | LAK 2014 |
Country/Territory | United States of America |
City | Indianapolis |
Period | 24/03/14 → 28/03/14 |
Internet address |
Keywords
- Collaboration
- Learning analytics
- Machine learning
- Theory