L@S 2016, the Third Annual Meeting of the ACM Conference on Learning at Scale, will be held April 25 and 26, 2016 at the University of Edinburgh, Scotland
It will be co-located with the 6th International Learning Analytics and Knowledge Conference, LAK 2016, April 25-29, 2016.
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.
- Full papers:
Oct 18, 2015 - submission deadline Oct 31, 2015 - submission deadline Dec 14, 2015 - notification of acceptance Feb 10, 2016 - camera-ready copy
- Work-in-progress and demonstration papers:
Jan 15, 2016 - submission deadline Feb 10, 2016 - notification of acceptance Feb 17, 2016 - camera-ready copy Extension: Feb 22, 2016 - camera-ready copy
- April 25-26, 2016 - Learning@Scale Conference.
No extensions will be given.
We solicit paper submissions reporting on rigorous research on methodologies, studies, analyses, tools, or technologies for learning at scale. Learning at Scale includes MOOCs, games (including massively multiplayer online games), citizen science communities, and other types of learning environments which (a) provide learning experiences to large number of learners and/or (b) produce detailed, high volume data about the learning process. Papers that tackle specific aspects of scale are particularly encouraged, for example, papers that deal with learning or educational phenomena that can only occur, be supported, or be observed with very large numbers of students, or in which the system improves after being exposed to data from previous use by many students.
Example topics include but are not limited to:
- 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
We invite full paper, shorter papers reporting on work-in-progress, and demonstrations.
Full papers must not exceed 10 pages (shorter is fine) and must use the ACM CHI Archive Format, available in latex and Word. Submissions must be in PDF format, written in English, contain original work and not be under review for any other venue while under review for this conference. All papers should be submitted through the EasyChair.
In order to increase high quality papers and independent merit, the evaluation process will be double blind. The papers submitted for review MUST NOT contain the authors' names, affiliations, or any information that may disclose the authors' identity (this information is to be restored in the camera-ready version upon acceptance). Please replace author names and affiliations with Xs on submitted papers. In particular, in the version submitted for review please avoid explicit auto-references, such as "in  we show" -- consider "in  it is shown". I.e., you should cite your own relevant previous work, so that a reviewer can access it and see the new contributions, but yet the text should be written so that it does not state that the cited work belongs to the authors.
A Work-in-Progress (WiP) is a concise report of recent findings or other types of innovative or thought-provoking work that has not yet reached a level of completion that would warrant submission of a full paper. Topics are the same as those listed for full papers.
At the conference, all accepted WiP submissions will be presented in poster form. Selected WiPs may be invited for oral presentation during the conference. Rejected full-papers can be resubmitted as WiP and will be evaluated accordingly.
Formatting: Work-in-Progress submissions 4 pages or fewer in length in the Extended Abstracts Format and submitted as a PDF file. Due to the very rapid selection process we cannot offer any extensions to the deadline. WiP submissions are not anonymous and should therefore include all author names, affiliations and contact information. If accepted, you should expect to prepare a poster to present at the conference venue. WiP submissions should be submitted through the EasyChair.
Demonstrations show aspects of learning at scale in an interactive hands-on form. A live demonstration is a great opportunity to communicate ideas and concepts in a powerful way that a regular presentation cannot. We invite demonstrations of learning and analytical environments and other systems that have direct relevance to learning at scale. We especially encourage authors of accepted papers and industrial partners to showcase their technologies using this format. Demonstration submissions are 2 pages or fewer in length in the Extended Abstracts Format and submitted as a PDF file. A demonstration proposal should address two components:
- The merit and nature of the demonstrated technology. If the proposed demonstration is associated with a Full Paper or a WiP submission, please point to the title of the submission instead of repeating the information here.
- Details of how the demo will be executed in practice, and how visitors will interact with it during the conference.
Proposals for demonstrations should be submitted through the EasyChair.
Full papers will appear in the conference proceedings published by the ACM Press in the ACM Digital Library. Work-in-Progress and Demonstration papers will appear in a separate part of the conference proceedings. The status of Work-in-Progress paper will be akin to what CHI describes as "semi-archival", meaning the results reported in the WIP must be original, but copyright is retained by the authors and the material can be used as the basis for future publications in ACM venues as long as there are significant revisions from the original.
Proceedings from prior years can be accessed here:
L@S 2014 Proceedings
L@S 2015 Proceedings
Full Papers and Work in Progress should be submitted at EasyChair, https://easychair.org/conferences/?conf=las2016.
Please direct all inquiries to: email@example.com
Jeff Haywood, the University of Edinburgh, UK
Vincent Aleven, Carnegie Mellon University, USA
Judy Kay, University of Sydney, Australia
Ido Roll, University of British Columbia, Canada
Local Organization Chair
Dragan Gasevic, the University of Edinburgh, UK