| Record Type: |
Electronic resources
: Monograph/item
|
| Title/Author: |
Theory informing and arising from learning analytics/ edited by Kathryn Bartimote, Sarah K. Howard, Dragan Gašević. |
| other author: |
Bartimote, Kathryn. |
| Published: |
Cham :Springer Nature Switzerland : : 2024., |
| Description: |
x, 221 p. :ill. (some col.), digital ;24 cm. |
| [NT 15003449]: |
Part I State of the Art Theory and Learning Analytics -- Theory and learning analytics a historical perspective -- Making bigger waves Automating theoretical coding to generate educationally -- In Conversation Gulson Anderson & Prinsloo Examining theoretical approaches and future directions for ethics in learning analytics -- Part II Theory Application in Practice Answering Questions and Increasing the Meaningfulness of Learning Analytics Research -- In Conversation Bannert Molenaar & Winne Multiple perspectives on researching and supporting self regulated learning via analytics -- Learning analytics Framework for Analysing Regulation in Collaborative Learning -- Theory and intermediate level knowledge in Multimodal Learning Analytics -- Collaborative learning theory and analytics -- Emotion theory and learning analytics A theoretical framework for capturing emotion regulation using process data -- Integrating theories of learning and social networks in learning analytics -- What could learning analytics learn from Human Computer Interaction theory -- Part III Innovative Theory Uses and Possibilities in Learning Analytics -- In conversation Baker Järvelä & Shaffer The relationship between computational methods and theory in learning analytics -- Theories all the way across The role of theory in learning analytics and the case for unified methods -- Towards a genealogical critical theory of learning analytics. |
| Contained By: |
Springer Nature eBook |
| Subject: |
Educational statistics. - |
| Online resource: |
https://doi.org/10.1007/978-3-031-60571-0 |
| ISBN: |
9783031605710 |