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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
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