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|Acronym=EDM 2019 | |Acronym=EDM 2019 | ||
|Title=12th International Conference on Educational Data Mining | |Title=12th International Conference on Educational Data Mining | ||
| − | | | + | |In Event Series=Event Series:EDM |
| − | | | + | |Single Day Event=no |
| − | | | + | |Start Date=2019/07/02 |
| + | |End Date=2019/07/05 | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=on site | ||
| + | |Venue=Boulevard Robert-Bourassa 777 | ||
|City=Montreal | |City=Montreal | ||
|Region=Quebec | |Region=Quebec | ||
|Country=Country:CA | |Country=Country:CA | ||
| + | |Academic Field=Computer Based Learning; Data Mining; Educational Data Mining | ||
| + | |Official Website=http://educationaldatamining.org/edm2019/ | ||
|Submission Link=https://easychair.org/conferences/?conf=edm2019 | |Submission Link=https://easychair.org/conferences/?conf=edm2019 | ||
| + | |DOI=10.25798/s3xv-6a78 | ||
| + | |Type=Conference | ||
| + | |Twitter account=@EDM2019MTL | ||
|has general chair=Michel Desmarais, Roger Nkambou | |has general chair=Michel Desmarais, Roger Nkambou | ||
|has program chair=Collin Lynch, Agathe Merceron | |has program chair=Collin Lynch, Agathe Merceron | ||
|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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|has Proceedings Bibliography=drive.google.com/file/d/1vptlF7O5MpVfPTMEHcQPacN0zhJk6u54/view | |has Proceedings Bibliography=drive.google.com/file/d/1vptlF7O5MpVfPTMEHcQPacN0zhJk6u54/view | ||
|pageCreator=User:Curator 73 | |pageCreator=User:Curator 73 | ||
|pageEditor=User:Curator 55 | |pageEditor=User:Curator 55 | ||
|contributionType=1 | |contributionType=1 | ||
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}} | }} | ||
{{Event Deadline | {{Event Deadline | ||
| + | |Notification Deadline=2019/04/11 | ||
|Paper Deadline=2019/03/04 | |Paper Deadline=2019/03/04 | ||
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|Camera-Ready Deadline=2019/05/01 | |Camera-Ready Deadline=2019/05/01 | ||
|Submission Deadline=2019/03/04 | |Submission Deadline=2019/03/04 | ||
| + | }} | ||
| + | {{Organizer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=International Educational Data Mining Society | ||
| + | }} | ||
| + | {{Event Metric | ||
| + | |Number Of Submitted Papers=185 | ||
| + | |Number Of Accepted Papers=64 | ||
}} | }} | ||
{{S Event}} | {{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. | ||
| − | * | + | *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. |
Latest revision as of 11:50, 4 August 2023
Deadlines
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Metrics
Submitted Papers
185
Accepted Papers
64
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.
