LearnScale 2014 - ACM Conference on Learning at Scale

The first annual meeting of the ACM Conference on Learning at Scale will be held March 4-5, 2014 in Atlanta, GA, USA. This conference is intended to promote scientific exchange of interdisciplinary research at the intersection of the learning sciences and computer science. Inspired by the emergence of Massive Open Online Courses (MOOCs) and the accompanying huge shift in thinking about education, this conference was created by ACM as a new scholarly venue and key focal point for the review and presentation of the highest quality research on how learning and teaching can change and improve when done at scale.
"Learning at Scale" refers to new approaches for students to learn and for teachers to teach, when engaging large numbers of students, either in a face-to-face setting or remotely, whether synchronous or asynchronous, with the requirement that the techniques involve large numbers of students (where "large" is preferably thousands of students, but can also apply to hundreds in in-person settings). Topics include, but are not limited to: Usability Studies, Tools for Automated Feedback and Grading, Learning Analytics, Analysis of Log Data, Studies of Application of Existing Learning Theory, Investigation of Student Behavior and Correlation with Learning Outcomes, New Learning and Teaching Techniques at Scale.
All papers must tackle topics "at scale." For example, a paper that would not qualify for Learning at Scale would be one about an intelligent tutoring system that behaves no differently with one student than with thousands, or which does not improve after being exposed to data from previous use by many students; such work should instead be submitted to a conference like ITS or AIED.
ACM Learning at Scale 2014 is the first in a new conference series. All full papers accepted will be published in the ACM Digital Library as archival publications. While we encourage visionary and forward-looking papers, please only submit your best novel work. The paper track will not accept work recently published or soon to be published in another conference or journal. However, to encourage exchange of ideas, such work can be submitted to the non-archival work-in-progress and demo track. For submissions of this kind, the authors should include the venue of previous or concurrent publication.
SUBMISSIONS
FULL PAPERS (8-10 PAGES)
We solicit rigorous research on methodologies, studies, analyses, tools, or technologies for learning at scale, with example topics including but not limited to:
Usability studies and effectiveness studies of designs of user interface elements for students or instructors, including:
Discussion forums
Status indicators of student progress
Status indicators of instructor effectiveness
Instructional video design for MOOCs
Tools and pedagogy to promote community or increase retention in MOOCs and large-scale in-person courses
Log analysis of student behavior for:
Assessing reasons for student outcome as determined by modifying tool design
Modeling students based on responses to variations in tool design
Data-driven personalization
Evaluation strategies such as quiz design
Studies of applications of existing learning theory to the MOOC context (peer learning, project based learning, etc.)
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
Investigation of observable student behaviors and their correlation if any with learning outcomes, e.g.:
Analyses of discussion forums to see if quantity and quality correlate with learning
How to motivate students to post on forums
Do lurkers (those who don't post) learn as much as those who post?
What are the roles of leaders in student group interactions/discussions?
Improvements to learning, community, and pedagogy in large-scale in-person and blended online and in-person courses
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 [1] we show" -- consider "in [1] it is shown". I.e., you may cite your own previous works provided that it is not deducible from the text that the cited work belongs to the authors. When citing your previous work, please keep the names with Xs. Papers not following this policy will be rejected.
Full papers must not exceed 10 pages (and shorter is OK) and must use the ACM style. Papers that deviate from this style or exceed the maximum length will be rejected. 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.
Accepted full papers will be allotted 20 minutes for presentation. Some papers that are good but not strong enough to be full papers may be allotted fewer pages and assigned 10 minutes for presentation, or may be recommended for poster presentation; the authors can choose whether to accept this condition or withdraw the submission. These papers will all appear in the conference proceedings.