View source for L@S 2019

{{Event
|Acronym=L@S 2019
|Title=6th ACM Conference on Learning at Scale
|Series=L@S
|Type=Conference
|Start date=2019/06/24
|End date=2019/06/25
|Homepage=https://learningatscale.acm.org/las2019/
|Twitter account=@LearningAtScale
|City=Chicago
|State=Illinois
|Country=USA
|Has host organization=Association for Computing Machinery (AMC)
|has general chair=David Joyner
|has program chair=John C. Mitchell, Kaska Porayska-Pomsta
|has Proceedings Link=https://dl.acm.org/doi/proceedings/10.1145/3330430
}}
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
Acronym	L@S 2019 +
End date	June 25, 2019 +
Event in series	L@S +
Event type	Conference +
Has coordinates	41° 52' 32", -87° 37' 28"Latitude: 41.875561111111 Longitude: -87.624422222222 +
Has general chair	David Joyner +
Has location city	Chicago +
Has location country	Category:USA +
Has location state	Illinois +
Has program chair	John C. Mitchell + and Kaska Porayska-Pomsta +
Has twitter	@LearningAtScale +
Homepage	https://learningatscale.acm.org/las2019/ +
IsA	Event +
Start date	June 24, 2019 +
Title	6th ACM Conference on Learning at Scale +