Difference between revisions of "Event:L@S 2019"

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

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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

@@ Line 2: / Line 2: @@
 |Acronym=L@S 2019
 |Title=6th ACM Conference on Learning at Scale
-|Type=Conference
+|In Event Series=Event Series:L@S
-|Official Website=https://learningatscale.acm.org/las2019/
+|Single Day Event=no
-|Twitter account=@LearningAtScale
+|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
+|Twitter account=@LearningAtScale
 |has general chair=David Joyner
 |has program chair=John C. Mitchell, Kaska Porayska-Pomsta
@@ Line 14: / Line 21: @@
 |pageEditor=User:Curator 27
 |contributionType=1
-|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
 }}
 {{Event Deadline}}
-{{S Event}}
 {{Organizer
+|Contributor Type=organization
 |Organization=Association for Computing Machinery (AMC)
-|Contributor Type=organization
 }}
+{{Event Metric}}
+{{S Event}}
 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.
+*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.
+*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
+*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 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.
+*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
+*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:
+*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
+*Best practices in open scie nce, including pre-planning and pre-registration
-*         Alternatives to conducting and reporting null hypothesis significance testing
+*Alternatives to conducting and reporting null hypothesis significance testing
-*         Best practices in the archiving and reuse of learner data in safe, ethical ways
+*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
+*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.
+*Tools or techniques for personalization and adaptation, based on log data, user modeling, or choice.
-*     Approaches to fostering inclusive education at scale, such as:
+*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 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
+*The application of insights from small-scale learning communities to large-scale learning environments
-*         Learning environments for neurodevelopmental, cultural, and socio-economic diversity
+*Learning environments for neurodevelopmental, cultural, and socio-economic diversity
-*     Usability, efficacy and effectiveness studies of design elements for students or instructors, such as:
+*Usability, efficacy and effectiveness studies of design elements for students or instructors, such as:
-*         Status indicators of student progress or  instructional effectiveness
+*Status indicators of student progress or  instructional effectiveness
-*         Methods to promote community, support learning, or increase retention at scale
+*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
+*Tools and pedagogy such as open learner models, to promote self-efficacy, self-regulation and motivation
-*     Log analysis of student behaviour, e.g.:
+*Log analysis of student behaviour, e.g.:
-*         Assessing reasons for student outcome as determined by modifying tool design
+*Assessing reasons for student outcome as determined by modifying tool design
-*         Modelling learners based on responses to variations in tool design
+*Modelling learners based on responses to variations in tool design
-*         Evaluation strategies such as quiz or discussion forum design
+*Evaluation strategies such as quiz or discussion forum design
-*         Instrumenting systems and data representation to capture relevant indicators of learning
+*Instrumenting systems and data representation to capture relevant indicators of learning
-*     New tools and techniques for learning at scale, such as:
+*New tools and techniques for learning at scale, such as:
-*         Games for learning at scale
+*Games for learning at scale
-*         Automated feedback tools, such as for essay writing, programming, and so on
+*Automated feedback tools, such as for essay writing, programming, and so on
-*         Automated grading tools
+*Automated grading tools
-*         Tools for interactive tutoring
+*Tools for interactive tutoring
-*         Tools for learner modelling
+*Tools for learner modelling
-*         Tools for increasing learner autonomy in learning and self-assessment
+*Tools for increasing learner autonomy in learning and self-assessment
-*         Tools for representing learner models
+*Tools for representing learner models
-*         Interfaces for harnessing learning data at scale
+*Interfaces for harnessing learning data at scale
-*         Innovations in platforms for supporting learning at scale
+*Innovations in platforms for supporting learning at scale
-*         Tools to support for capturing, managing learning data
+*Tools to support for capturing, managing learning data
-*         Tools and techniques for managing privacy of 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.