Third Annual ACM Conference on Learning at Scale (L@S 2016)
Views: 761
When : 2016-04-25
Where : United Kingdom
Submission Deadline : 2015-10-18
Categories : Computer Science & Information Technology , Machine Learning
Third Annual ACM Conference on Learning at Scale (L@S 2016)
Apr 25-26, 2016
Edinburgh, United Kingdom
Call For Papers
The conference is at the intersection of computer science and the learning sciences, seeking to improve practice and theories of learning at scale. Strong submissions typically build on relevant research and frameworks beyond a single home discipline. The program committee is multidisciplinary and will expect that contributions expand what is known when the state of the art of several relevant source literatures is considered.
Topics
* Usability studies and effectiveness studies of design elements for students or instructors, including:* Status indicators of student progress
* Status indicators of instructor 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.
* Personalization and adaptation, based on log data, user modeling, or choice.
* Studies of applications of existing learning theories to the MOOC context (peer learning, project based learning, etc.).
* Informing theories of learning at scale.
* Large online learning in the developing world
* 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
* 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
* Investigation of observable student behaviors and their correlation if any with learning, e.g.:
* What do more successful learners do more of?
* What do more successful instructors do more of?
* Self- and co-regulation of learning at scale
* Collaborative learning in courses that have scale
* Depth and retention of learning and understanding
* Improvements to learning, community, and pedagogy in large-scale in-person and blended online and in-person courses
* Instructional principles for learning at scale
* Facilitation of informal subcommunities