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|Title=12th International Conference on Educational Data Mining | |Title=12th International Conference on Educational Data Mining | ||
|Type=Conference | |Type=Conference | ||
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|Homepage=http://educationaldatamining.org/edm2019/ | |Homepage=http://educationaldatamining.org/edm2019/ | ||
|Twitter account=@EDM2019MTL | |Twitter account=@EDM2019MTL | ||
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|Region=Quebec | |Region=Quebec | ||
|Country=Country:CA | |Country=Country:CA | ||
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|Submitting link=https://easychair.org/conferences/?conf=edm2019 | |Submitting link=https://easychair.org/conferences/?conf=edm2019 | ||
|has general chair=Michel Desmarais, Roger Nkambou | |has general chair=Michel Desmarais, Roger Nkambou | ||
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|has workshop chair=Luc Paquette, Cristobol Romero | |has workshop chair=Luc Paquette, Cristobol Romero | ||
|Has PC member=Akram Bita, Giora Alexandron, Anne Boyer, Mirjam Augstein, Costin Badica | |Has PC member=Akram Bita, Giora Alexandron, Anne Boyer, Mirjam Augstein, Costin Badica | ||
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|Submitted papers=185 | |Submitted papers=185 | ||
|Accepted papers=64 | |Accepted papers=64 | ||
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|Event Mode=on site | |Event Mode=on site | ||
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| + | {{Event Deadline | ||
| + | |Paper Deadline=2019/03/04 | ||
| + | |Notification Deadline=2019/04/11 | ||
| + | |Camera-Ready Deadline=2019/05/01 | ||
| + | |Submission Deadline=2019/03/04 | ||
| + | }} | ||
| + | {{S Event}} | ||
== Topics == | == Topics == | ||
Topics of interest to the conference include but are not limited to. | Topics of interest to the conference include but are not limited to. | ||
Revision as of 19:23, 22 September 2022
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Venue
Boulevard Robert-Bourassa 777, Montreal, Quebec, Canada
Topics
Topics of interest to the conference include but are not limited to.
- Modeling student and group interaction for guidance and collaborative problem-solving.
- Deriving representations of domain knowledge from data.
- Modeling real-world problem-solving in open-ended domains.
- Detecting and addressing students’ affective and emotional states.
- Informing data mining research with educational theory.
- Developing new techniques for mining educational data.
- Data mining to understand how learners interact in formal and informal educational contexts.
- Modeling students’ affective states and engagement with multimodal data.
- Synthesizing rich data to inform students and educators.
- Bridging data mining and learning sciences.
- Applying social network analysis to support student interactions.
- Legal and social policies to govern EDM.
- Developing generic frameworks, techniques, research methods, and approaches for EDM.
- Closing the loop between EDM research and educational outcomes to yield actionable advice.
- Automatically assessing student knowledge.
