Difference between revisions of "EDM 2020"
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|has Keynote speaker=Alina von Davier, Abelardo Pardo, Kobi Gal | |has Keynote speaker=Alina von Davier, Abelardo Pardo, Kobi Gal | ||
}} | }} | ||
| − | + | ''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 | + | '''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. | 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. | 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. | ||
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== 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: |
| − | |||
| − | |||
* Causal inference of which factors impact -not just predict- students’ learning. | * 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. | * Developing and applying fairer learning algorithms that exhibit similar performance across subgroups of students, and detecting instances of algorithmic unfairness in existing methods. | ||
Latest revision as of 10:39, 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.
Facts about "EDM 2020"
| Acronym | EDM 2020 + |
| Camera ready due | May 6, 2020 + |
| End date | July 13, 2020 + |
| Event in series | EDM + |
| Event type | Conference + |
| Has Keynote speaker | Alina von Davier +, Abelardo Pardo + and Kobi Gal + |
| Has PC member | Agathe Merceron +, Andrew Olney +, Bradford Mott +, Collin Lynch + and Dragan Gasevic + |
| Has Submitting link | https://easychair.org/my/conference?conf=edm-2020 + |
| Has coordinates | 33° 31' 39", -5° 6' 27"Latitude: 33.527605555556 Longitude: -5.1074083333333 + |
| Has general chair | Violetta Cavalli-Sforza + and Cristobal Romero + |
| Has location city | Ifrane + |
| Has location country | Category:Morocco + |
| Has program chair | Anna Rafferty + and Jacob Whitehill + |
| Has workshop chair | François Bouchet + and Vanda Luengo + |
| Homepage | http://educationaldatamining.org/edm2020/ + |
| IsA | Event + |
| Notification | April 16, 2020 + |
| Start date | July 10, 2020 + |
| Submission deadline | March 9, 2020 + |
| Title | Educational Data Mining 2020 + |
