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|Acronym=L@S 2019 | |Acronym=L@S 2019 | ||
|Title=6th ACM Conference on Learning at Scale | |Title=6th ACM Conference on Learning at Scale | ||
| + | |In Event Series=Event Series:L@S | ||
| + | |Single Day Event=no | ||
| + | |Start Date=2019/06/24 | ||
| + | |End Date=2019/06/25 | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=on site | ||
| + | |City=Chicago | ||
| + | |Region=Illinois | ||
| + | |Country=Country:US | ||
| + | |Official Website=https://learningatscale.acm.org/las2019/ | ||
| + | |DOI=10.25798/tmqh-nh98 | ||
|Type=Conference | |Type=Conference | ||
| − | |||
|Twitter account=@LearningAtScale | |Twitter account=@LearningAtScale | ||
| − | |||
| − | |||
| − | |||
| − | |||
|has general chair=David Joyner | |has general chair=David Joyner | ||
|has program chair=John C. Mitchell, Kaska Porayska-Pomsta | |has program chair=John C. Mitchell, Kaska Porayska-Pomsta | ||
| Line 15: | Line 21: | ||
|pageEditor=User:Curator 27 | |pageEditor=User:Curator 27 | ||
|contributionType=1 | |contributionType=1 | ||
| − | |||
| − | |||
| − | |||
| − | |||
}} | }} | ||
| + | {{Event Deadline}} | ||
| + | {{Organizer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=Association for Computing Machinery (AMC) | ||
| + | }} | ||
| + | {{Event Metric}} | ||
| + | {{S Event}} | ||
Example topics: Specific topics of relevance include, but are not limited to: | Example topics: Specific topics of relevance include, but are not limited to: | ||
| − | * | + | * |
| − | * | + | *Novel assessments of learning, including those drawing on computational techniques for automated, peer, or human-assisted assessment. |
| − | * | + | *New methods for validating inferences about human learning from established measures, assessments, or proxies. |
| − | * | + | *Experimental interventions that show evidence of improved learning outcomes, such as |
| − | * | + | *Domain independent interventions inspired by social psychology, behavioural economics, and related fields, including those with the potential to benefit learners from diverse socio-economic and cultural backgrounds |
| − | * | + | *Domain specific interventions inspired by discipline-based educational research that may advance teaching and learning of specific ideas or theories within a field or redress misconceptions. |
| − | * | + | *Heterogeneous treatment effects in large experiments that point the way towards personalized or adaptive interventions |
| − | * | + | *Methodological papers that address challenges emerging from the “replication crisis” and “new statistics” in the context of Learning at Scale research: |
| − | * | + | *Best practices in open scie nce, including pre-planning and pre-registration |
| − | * | + | *Alternatives to conducting and reporting null hypothesis significance testing |
| − | * | + | *Best practices in the archiving and reuse of learner data in safe, ethical ways |
| − | * | + | *Advances in differential privacy and other methods that reconcile the opportunities of open science with the challenges of privacy protection |
| − | * | + | *Tools or techniques for personalization and adaptation, based on log data, user modeling, or choice. |
| − | * | + | *Approaches to fostering inclusive education at scale, such as: |
| − | * | + | *The blended use of large-scale learning environments in specific residential or small-scale learning communities, or the use of sub-groups or small communities within large-scale learning environments |
| − | * | + | *The application of insights from small-scale learning communities to large-scale learning environments |
| − | * | + | *Learning environments for neurodevelopmental, cultural, and socio-economic diversity |
| − | * | + | *Usability, efficacy and effectiveness studies of design elements for students or instructors, such as: |
| − | * | + | *Status indicators of student progress or instructional effectiveness |
| − | * | + | *Methods to promote community, support learning, or increase retention at scale |
| − | * | + | *Tools and pedagogy such as open learner models, to promote self-efficacy, self-regulation and motivation |
| − | * | + | *Log analysis of student behaviour, e.g.: |
| − | * | + | *Assessing reasons for student outcome as determined by modifying tool design |
| − | * | + | *Modelling learners based on responses to variations in tool design |
| − | * | + | *Evaluation strategies such as quiz or discussion forum design |
| − | * | + | *Instrumenting systems and data representation to capture relevant indicators of learning |
| − | * | + | *New tools and techniques for learning at scale, such as: |
| − | * | + | *Games for learning at scale |
| − | * | + | *Automated feedback tools, such as for essay writing, programming, and so on |
| − | * | + | *Automated grading tools |
| − | * | + | *Tools for interactive tutoring |
| − | * | + | *Tools for learner modelling |
| − | * | + | *Tools for increasing learner autonomy in learning and self-assessment |
| − | * | + | *Tools for representing learner models |
| − | * | + | *Interfaces for harnessing learning data at scale |
| − | * | + | *Innovations in platforms for supporting learning at scale |
| − | * | + | *Tools to support for capturing, managing learning data |
| − | * | + | *Tools and techniques for managing privacy of learning data |
The conference is co-located with and immediately precedes the 2019 International Conference on AI in Education in the same city and venue. | The conference is co-located with and immediately precedes the 2019 International Conference on AI in Education in the same city and venue. | ||
| Line 64: | Line 73: | ||
The conference organizers are: | The conference organizers are: | ||
| − | John C. Mitchell, Stanford University, Program Co- | + | John C. Mitchell, Stanford University, Program Co-Chair |
| − | Kaska Porayska-Pomsta, University College London, Program Co- | + | Kaska Porayska-Pomsta, University College London, Program Co-Chair |
| − | David Joyner, Georgia Institute of Technology, General | + | David Joyner, Georgia Institute of Technology, General Chair |
Latest revision as of 12:24, 7 July 2023
The document "L@S 2019" was published on "2023-07-07T12:24:19" on the website "ConfIDent" under the URL https://confident-conference.org/index.php/Event:L@S 2019.
The document "L@S 2019" describes an event in the sense of a conference.
The document "L@S 2019" contains information about the event "L@S 2019" with start date "2019/06/24" and end date "2019/06/25".
The event "L@S 2019" is part of the event series identified by [[Event Series:L@S]]
Deadlines
Metrics
Venue
Chicago, Illinois, United States of America
Warning: Venue is missing. The map might not show the exact location.
Example topics: Specific topics of relevance include, but are not limited to:
- Novel assessments of learning, including those drawing on computational techniques for automated, peer, or human-assisted assessment.
- New methods for validating inferences about human learning from established measures, assessments, or proxies.
- Experimental interventions that show evidence of improved learning outcomes, such as
- Domain independent interventions inspired by social psychology, behavioural economics, and related fields, including those with the potential to benefit learners from diverse socio-economic and cultural backgrounds
- Domain specific interventions inspired by discipline-based educational research that may advance teaching and learning of specific ideas or theories within a field or redress misconceptions.
- Heterogeneous treatment effects in large experiments that point the way towards personalized or adaptive interventions
- Methodological papers that address challenges emerging from the “replication crisis” and “new statistics” in the context of Learning at Scale research:
- Best practices in open scie nce, including pre-planning and pre-registration
- Alternatives to conducting and reporting null hypothesis significance testing
- Best practices in the archiving and reuse of learner data in safe, ethical ways
- Advances in differential privacy and other methods that reconcile the opportunities of open science with the challenges of privacy protection
- Tools or techniques for personalization and adaptation, based on log data, user modeling, or choice.
- Approaches to fostering inclusive education at scale, such as:
- The blended use of large-scale learning environments in specific residential or small-scale learning communities, or the use of sub-groups or small communities within large-scale learning environments
- The application of insights from small-scale learning communities to large-scale learning environments
- Learning environments for neurodevelopmental, cultural, and socio-economic diversity
- Usability, efficacy and effectiveness studies of design elements for students or instructors, such as:
- Status indicators of student progress or instructional effectiveness
- Methods to promote community, support learning, or increase retention at scale
- Tools and pedagogy such as open learner models, to promote self-efficacy, self-regulation and motivation
- Log analysis of student behaviour, e.g.:
- Assessing reasons for student outcome as determined by modifying tool design
- Modelling learners based on responses to variations in tool design
- Evaluation strategies such as quiz or discussion forum design
- Instrumenting systems and data representation to capture relevant indicators of learning
- New tools and techniques for learning at scale, such as:
- Games for learning at scale
- Automated feedback tools, such as for essay writing, programming, and so on
- Automated grading tools
- Tools for interactive tutoring
- Tools for learner modelling
- Tools for increasing learner autonomy in learning and self-assessment
- Tools for representing learner models
- Interfaces for harnessing learning data at scale
- Innovations in platforms for supporting learning at scale
- Tools to support for capturing, managing learning data
- Tools and techniques for managing privacy of learning data
The conference is co-located with and immediately precedes the 2019 International Conference on AI in Education in the same city and venue.
The conference organizers are:
John C. Mitchell, Stanford University, Program Co-Chair Kaska Porayska-Pomsta, University College London, Program Co-Chair David Joyner, Georgia Institute of Technology, General Chair
