We encourage diverse topical submissions to our conference, and example topics include but are not limited to the following topics. In all topics, we encourage a particular focus on contexts and populations that have been historically not well served.

• Novel assessments of learning, 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 in large-scale learning environments that show evidence of improved learning outcomes

• Evidence of heterogenous treatment effects in large experiments that point the way towards potential personalized or adaptive interventions

• Domain independent interventions inspired by social psychology, behavioral economics, and related fields with the potential to benefit learners in diverse fields and disciplines

• Domain specific interventions inspired by discipline-based educational research that have the potential to advance teaching and learning of specific ideas, misconceptions, and theories within a field

• 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 science, 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.

• 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

• Usability studies and effectiveness studies of design elements for students or instructors, including:

• Status indicators of student progress

• Status indicators of instructional effectiveness

• Tools and pedagogy to promote community, support learning, or increase retention in at-scale environments

• Log analysis of student behavior, e.g.:

• Assessing reasons for student outcome as determined by modifying tool design

• Modeling students 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, including:

• Games for learning at scale

• Automated feedback tools (for essay writing, programming, etc)

• Automated grading tools

• Tools for interactive tutoring

• Tools for learner modeling

• 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