Difference between revisions of "EDM 2020"

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|City=Ifrane
 
|City=Ifrane
 
|Country=Morocco
 
|Country=Morocco
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|Notification=2020/04/16
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|Camera ready=2020/05/06
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|Submitting link=https://easychair.org/my/conference?conf=edm-2020
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|has general chair=Violetta Cavalli-Sforza, Cristobal Romero
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|has program chair=Anna Rafferty, Jacob Whitehill
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|has workshop chair=François Bouchet, Vanda Luengo
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|Has PC member=Agathe Merceron, Andrew Olney, Bradford Mott, Collin Lynch, Dragan Gasevic
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|has Keynote speaker=Alina von Davier, Abelardo Pardo, Kobi Gal
 
}}
 
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<!-- PLEASE ADAPT OR DELETE THIS PART COMPLETELY - You can just paste in the call for papers and remove this and the last line
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''Due to the global health emergency caused by the Coronavirus pandemic, EDM2020 will take place as a Fully Virtual Conference''
  
==Topics==
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'''Improving Learning Outcomes for All Learners'''
==Submissions==
 
==Important Dates==
 
  
==Committees==
+
Educational Data Mining is a leading international forum for high-quality research that mines datasets to answer educational research questions, including exploring how people learn and how they teach. These data may originate from a variety of learning contexts, including learning and information management systems, interactive learning environments, intelligent tutoring systems, educational games, and data-rich learning activities. Educational data mining considers a wide variety of types of data, including but not limited to raw log files, student-produced artifacts, discourse, multimodal streams such as eye-tracking and other sensor data, and additional databases of student information. The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners.
* Co-Organizers
 
* General Co-Chairs
 
** [[has general chair::some person]], some affiliation, country
 
  
* PC Co-Chairs
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The theme of this year’s conference is “Improving Learning Outcomes for All Learners”. The theme comprises two parts: (1) Identifying actionable learning or teaching strategies that can be used to improve learning outcomes. (2) Using EDM to promoting more equitable learning across diverse groups of learners. For this 13th iteration of the conference we specifically welcome research that advances aforementioned areas.
** [[has program chair::some person]], some affiliation, country
 
  
* Workshop Chair
+
== Topics ==
** [[has workshop chair::some person]], some affiliation, country
 
  
* Panel Chair
+
Topics of interest to the conference include but are not limited to:   
** [[has OC member::some person]], some affiliation, country
+
* Causal inference of which factors impact -not just predict- students’ learning.
 
+
* Developing and applying fairer learning algorithms that exhibit similar performance across subgroups of students, and detecting instances of algorithmic unfairness in existing methods.
* Seminars Chair
+
* Replicating previous studies with larger sample sizes, in different domains, and/or in more diverse contexts.
** [[has tutorial chair::some person]], some affiliation, country
+
* Modeling student and group interaction for collaborative and/or competitive problem-solving.
 
+
* EDM for gamification and in educational games.
* Demonstration Co-Chairs
+
* Deriving representations of domain knowledge from data.
** [[has demo chair::some person]], some affiliation, country
+
* Modeling real-world problem solving in open-ended domains.
** [[has demo chair::some person]], some affiliation, country
+
* Modeling and detecting students’ affective states and cognitive states (e.g., engagement, confusion) with multimodal data    * Ethical considerations in EDM.
 
+
* Closing the loop between EDM research and educational outcomes to yield actionable advice.
* Local Organizing Co-Chairs
+
* Informing data mining research with educational and/or motivational theory.
** [[has local chair::some person]], some affiliation, country
+
* Developing new techniques for mining educational data.
 
+
* Data mining to understand how learners interact in formal and informal educational contexts.
* Program Committee Members
+
* Bridging the gap between data mining and learning sciences.
** [[has PC member::some person]], some affiliation, country
+
* Legal and social policies to govern EDM.
-->
+
* Automatically assessing student knowledge.
 +
* Social network analysis of student and teacher interactions.

Latest revision as of 11:39, 17 April 2020

EDM 2020
Educational Data Mining 2020
Event in series EDM
Dates 2020/07/10 (iCal) - 2020/07/13
Homepage: http://educationaldatamining.org/edm2020/
Submitting link: https://easychair.org/my/conference?conf=edm-2020
Location
Location: Ifrane, Morocco
Loading map...

Important dates
Submissions: 2020/03/09
Notification: 2020/04/16
Camera ready due: 2020/05/06
Committees
General chairs: Violetta Cavalli-Sforza, Cristobal Romero
PC chairs: Anna Rafferty, Jacob Whitehill
Workshop chairs: François Bouchet, Vanda Luengo
PC members: Agathe Merceron, Andrew Olney, Bradford Mott, Collin Lynch, Dragan Gasevic
Keynote speaker: Alina von Davier, Abelardo Pardo, Kobi Gal
Table of Contents

Contents


Due to the global health emergency caused by the Coronavirus pandemic, EDM2020 will take place as a Fully Virtual Conference

Improving Learning Outcomes for All Learners

Educational Data Mining is a leading international forum for high-quality research that mines datasets to answer educational research questions, including exploring how people learn and how they teach. These data may originate from a variety of learning contexts, including learning and information management systems, interactive learning environments, intelligent tutoring systems, educational games, and data-rich learning activities. Educational data mining considers a wide variety of types of data, including but not limited to raw log files, student-produced artifacts, discourse, multimodal streams such as eye-tracking and other sensor data, and additional databases of student information. The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners.

The theme of this year’s conference is “Improving Learning Outcomes for All Learners”. The theme comprises two parts: (1) Identifying actionable learning or teaching strategies that can be used to improve learning outcomes. (2) Using EDM to promoting more equitable learning across diverse groups of learners. For this 13th iteration of the conference we specifically welcome research that advances aforementioned areas.

Topics

Topics of interest to the conference include but are not limited to:

  • Causal inference of which factors impact -not just predict- students’ learning.
  • Developing and applying fairer learning algorithms that exhibit similar performance across subgroups of students, and detecting instances of algorithmic unfairness in existing methods.
  • Replicating previous studies with larger sample sizes, in different domains, and/or in more diverse contexts.
  • Modeling student and group interaction for collaborative and/or competitive problem-solving.
  • EDM for gamification and in educational games.
  • Deriving representations of domain knowledge from data.
  • Modeling real-world problem solving in open-ended domains.
  • Modeling and detecting students’ affective states and cognitive states (e.g., engagement, confusion) with multimodal data * Ethical considerations in EDM.
  • Closing the loop between EDM research and educational outcomes to yield actionable advice.
  • Informing data mining research with educational and/or motivational theory.
  • Developing new techniques for mining educational data.
  • Data mining to understand how learners interact in formal and informal educational contexts.
  • Bridging the gap between data mining and learning sciences.
  • Legal and social policies to govern EDM.
  • Automatically assessing student knowledge.
  • Social network analysis of student and teacher interactions.