Difference between revisions of "EDM 2019"
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|Title =12th International Conference on Educational Data Mining | |Title =12th International Conference on Educational Data Mining | ||
|Accepted papers=64 | |Accepted papers=64 | ||
| − | |Submitted papers=185}} | + | |Submitted papers=185 |
| + | }} | ||
== 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. | ||
Latest revision as of 04:32, 6 December 2021
Event Rating
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List of all ratings can be found at EDM 2019/rating
| EDM 2019 | |
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12th International Conference on Educational Data Mining
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| Event in series | EDM |
| Dates | 2019-07-02 (iCal) - 2019-07-05 |
| Homepage: | http://educationaldatamining.org/edm2019/ |
| Twitter account: | @EDM2019MTL |
| Submitting link: | https://easychair.org/conferences/?conf=edm2019 |
| Location | |
| Location: | CA/QC/Montreal, CA/QC, CA |
| Important dates | |
| Papers: | 2019/03/04 |
| Submissions: | 2019/03/04 |
| Notification: | 2019/04/11 |
| Camera ready due: | 2019/05/01 |
| Papers: | Submitted 185 / Accepted 64 (34.6 %) |
| Committees | |
| General chairs: | Michel Desmarais, Roger Nkambou |
| PC chairs: | Collin Lynch, Agathe Merceron |
| Workshop chairs: | Luc Paquette, Cristobol Romero |
| PC members: | Akram Bita, Giora Alexandron, Anne Boyer, Mirjam Augstein, Costin Badica |
| Keynote speaker: | Mike Mozer, Steve Ritter, Julita Vassileva |
| Table of Contents | |
| Tweets by @EDM2019MTL| colspan="2" style="padding-top: 2px; " | | |
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.