Measuring Affective and Motivational States as Conditions for Cognitive and Metacognitive Processing in Self-Regulated Learning-Reference-Cited by-同舟云学术

Measuring Affective and Motivational States as Conditions for Cognitive and Metacognitive Processing in Self-Regulated Learning

Published:2024-03-18 Issue: Volume: Page:
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Container-title:Proceedings of the 14th Learning Analytics and Knowledge Conference
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Author:

Raković Mladen¹^ORCID,Li Yuheng¹^ORCID,Foumani Navid Mohammadi²^ORCID,Salehi Mahsa²^ORCID,Kuhlmann Levin²^ORCID,Mackellar Geoffrey³^ORCID,Martinez-Maldonado Roberto¹^ORCID,Haffari Gholamreza²^ORCID,Swiecki Zachari¹^ORCID,Li Xinyu¹^ORCID,Chen Guanliang¹^ORCID,Gašević Dragan¹^ORCID

Affiliation:

1. Centre for Learning Analytics, Monash University, Australia

2. Department of Data Science and Artificial Intelligence, Monash University, Australia

3. Emotiv Research Pty Ltd, Australia

Funder

Australian Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3636555.3636934

Reference69 articles.

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3. Roger Azevedo and Dragan Gašević. 2019. Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: Issues and challenges. 207–210 pages.

4. Using Trace Data to Examine the Complex Roles of Cognitive, Metacognitive, and Emotional Self-Regulatory Processes During Learning with Multi-agent Systems

5. Roger Azevedo, Michelle Taub, and Nicholas V Mudrick. 2017. Understanding and reasoning about real-time cognitive, affective, and metacognitive processes to foster self-regulation with advanced learning technologies. In Handbook of self-regulation of learning and performance. Routledge, 254–270.

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