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|Acronym=EDM 2020 | |Acronym=EDM 2020 | ||
|Title=Educational Data Mining 2020 | |Title=Educational Data Mining 2020 | ||
| + | |Ordinal=13 | ||
| + | |In Event Series=Event Series:EDM | ||
| + | |Single Day Event=no | ||
| + | |Start Date=2020/07/10 | ||
| + | |End Date=2020/07/13 | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=online | ||
| + | |Academic Field=Computer Based Learning; Data Mining; Educational Data Mining | ||
| + | |Official Website=http://educationaldatamining.org/edm2020/ | ||
| + | |Submission Link=https://easychair.org/my/conference?conf=edm-2020 | ||
|Type=Conference | |Type=Conference | ||
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|has general chair=Violetta Cavalli-Sforza, Cristobal Romero | |has general chair=Violetta Cavalli-Sforza, Cristobal Romero | ||
|has program chair=Anna Rafferty, Jacob Whitehill | |has program chair=Anna Rafferty, Jacob Whitehill | ||
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|pageEditor=User:Curator 55 | |pageEditor=User:Curator 55 | ||
|contributionType=1 | |contributionType=1 | ||
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}} | }} | ||
{{Event Deadline | {{Event Deadline | ||
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|Submission Deadline=2020/03/09 | |Submission Deadline=2020/03/09 | ||
}} | }} | ||
| + | {{Organizer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=International Educational Data Mining Society | ||
| + | }} | ||
| + | {{Event Metric}} | ||
{{S Event}} | {{S Event}} | ||
''Due to the global health emergency caused by the Coronavirus pandemic, EDM2020 will take place as a Fully Virtual Conference'' | ''Due to the global health emergency caused by the Coronavirus pandemic, EDM2020 will take place as a Fully Virtual Conference'' | ||
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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. | 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== |
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. |
| − | * Replicating previous studies with larger sample sizes, in different domains, and/or in more diverse contexts. | + | *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. | + | *Modeling student and group interaction for collaborative and/or competitive problem-solving. |
| − | * EDM for gamification and in educational games. | + | *EDM for gamification and in educational games. |
| − | * Deriving representations of domain knowledge from data. | + | *Deriving representations of domain knowledge from data. |
| − | * Modeling real-world problem solving in open-ended domains. | + | *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. | + | *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. | + | *Closing the loop between EDM research and educational outcomes to yield actionable advice. |
| − | * Informing data mining research with educational and/or motivational theory. | + | *Informing data mining research with educational and/or motivational theory. |
| − | * Developing new techniques for mining educational data. | + | *Developing new techniques for mining educational data. |
| − | * Data mining to understand how learners interact in formal and informal educational contexts. | + | *Data mining to understand how learners interact in formal and informal educational contexts. |
| − | * Bridging the gap between data mining and learning sciences. | + | *Bridging the gap between data mining and learning sciences. |
| − | * Legal and social policies to govern EDM. | + | *Legal and social policies to govern EDM. |
| − | * Automatically assessing student knowledge. | + | *Automatically assessing student knowledge. |
| − | * Social network analysis of student and teacher interactions. | + | *Social network analysis of student and teacher interactions. |
Latest revision as of 08:37, 17 July 2023
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Venue
Warning: Venue is missing. The map might not show the exact location.
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.
