Difference between revisions of "L@S 2019"
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|has Proceedings Link=https://dl.acm.org/doi/proceedings/10.1145/3330430 | |has Proceedings Link=https://dl.acm.org/doi/proceedings/10.1145/3330430 | ||
|Acronym=L@S 2019 | |Acronym=L@S 2019 | ||
| − | |End date=2019 | + | |End date=2019-06-25 |
|Series=L@S | |Series=L@S | ||
|Type =Conference | |Type =Conference | ||
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|State=US/IL | |State=US/IL | ||
|City =US/IL/Chicago | |City =US/IL/Chicago | ||
| + | |Year =2019 | ||
|Homepage=https://learningatscale.acm.org/las2019/ | |Homepage=https://learningatscale.acm.org/las2019/ | ||
| − | |Start date=2019 | + | |Start date=2019-06-24 |
| − | |Title=6th ACM Conference on Learning at Scale | + | |Title=6th ACM Conference on Learning at Scale}} |
| − | }} | ||
Example topics: Specific topics of relevance include, but are not limited to: | Example topics: Specific topics of relevance include, but are not limited to: | ||
* | * | ||
Revision as of 03:55, 19 November 2021
Event Rating
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List of all ratings can be found at L@S 2019/rating
| L@S 2019 | |
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6th ACM Conference on Learning at Scale
| |
| Event in series | L@S |
| Dates | 2019-06-24 (iCal) - 2019-06-25 |
| Homepage: | https://learningatscale.acm.org/las2019/ |
| Twitter account: | @LearningAtScale |
| Location | |
| Location: | US/IL/Chicago, US/IL, US |
| Committees | |
| General chairs: | David Joyner |
| PC chairs: | John C. Mitchell, Kaska Porayska-Pomsta |
| Table of Contents | |
| Tweets by @LearningAtScale| colspan="2" style="padding-top: 2px; " | | |
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