L@S 2019

L@S 2019
Learning at Scale Conference
Event in series	L@S
Dates	2019/06/24 (iCal) - 2019/06/25
Homepage:	https://learningatscale.acm.org/las2019/
Location
Location:	Chicago, Illinois, USA
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Table of Contents

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