Call for Papers: Learning at Scale 2020
May 27-29, Georgia Tech, Atlanta, GA
We are excited to announce that the Learning at Scale (L@S) conference will be held May 27-29, 2020 with workshops on the 27th in Atlanta at Georgia Tech. We are inviting contributions that address innovations in scaling and enhancing learning, empirical investigations of learning at scale, new technical systems for learning at scale, and novel syntheses of relevant research. Work from both formal and informal education environments at all levels is encouraged; L@S welcomes studies of higher education and informal adult learning.
About Learning at Scale
L@S investigates large-scale, technology-mediated learning environments that typically have many active learners and few experts on hand to guide their progress or respond to individual needs. Modern learning at scale typically draws on data at scale, collected from current learners and previous cohorts of learners over time. Large-scale learning environments are very diverse. Formal institutional education in K-16 and campus-based courses in popular fields involve many learners, relative to the number of teaching staff, and leverage varying forms of data collection and automated support. Evolving forms of massive open online courses, mobile learning applications, intelligent tutoring systems, open courseware, learning games, citizen science communities, collaborative programming communities (e.g. Scratch), community tutorial systems (e.g. StackOverflow), shared critique communities (e.g. DeviantArt), and countless informal communities of learners (e.g. the Explain It Like I’m Five sub-Reddit) are all examples of learning at scale. All share a common purpose to increase human potential, leveraging data collection, data analysis, human interaction, and varying forms of computational assessment, adaptation and guidance.
Research on learning at scale naturally bring together two different research communities. Learning scientists are drawn to study established and emerging forms of knowledge development, transfer, modelling, and co-creation. Computer and data scientists are drawn to the specific and challenging needs for data collection, data sharing, analysis, computation, and interaction. The cornerstone of L@S is interdisciplinary research and progressive confluence toward more effective and varied future learning.
The L@S research community has become increasingly sophisticated, interdisciplinary and diverse. In the early years, researchers began by investigating proxy outcomes for learning, such as measures of participation, persistence, completion, satisfaction, and activity. Early MOOC researchers in particular documented correlations between easily observed measures of activity – videos watched, forum posts, clicks – and these outcome proxies. As the field and tools mature, however, we have increasing expectations for new and established measures of learning. As L@S research expands, we aim for more direct measures of student learning, accompanied by generalizable insight around instructional techniques, learning habits and experiences, technological infrastructures, and experimental interventions that improve learning outcomes.
The ACM Learning at Scale conference solicits original research paper submissions on methodologies, case studies, analyses, tools, or technologies for learning at scale, broadly construed. Four kinds of contributions will be accepted: Research Papers, Synthesis Papers, Work-in-Progress Posters, Demonstrations, and Workshops. Accepted papers and posters must be presented at the conference and will be included in the proceedings. Paper submissions, reviewing and notification to authors will be handled using Easy Chair. 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 authors will have the option of presenting supplementary online materials to aid in their presentation. Presenters are encouraged to use their allotted conference time for activities or discussion in addition to delivering presentations or showing posters. We encourage best practices in open science as described in the Statement on Open Science below.
Research Papers (up to 10 pages) – due Jan 20, 2020
We solicit empirical and theoretical papers on a diverse range of topics relevant to successful learning at scale. Consistent with past years, we welcome submission on: (1) design of systems for learning at scale, (2) effective learning interactions at scale, (3) understanding and supporting learners at scale. Accounts of robust methodologies from the learning sciences theory, practice, and/or the engineering perspectives are encouraged. Regardless of approach, strong contributions address relevance in terms of theory and practice.
For Learning@Scale 2020, we specifically solicit work in five areas of interest to grow our community whilst being inclusive to other work. Each area is represented by a community champion who can answer questions about the fit of potential submissions and who helps ensure a high-quality reviewing process in the area. The L@S 2020 areas of interest are:
- Causal Inference at Scale (Champion: Thomas Staubitz) — Studies that use digital learning environments with experimental designs to investigate factors that increase learning and refine theories by, for example, identifying sources of heterogeneity.
- Learning and Curriculum Analytics (Champion: Zach Pardos) — Studies that analyze large educational datasets with data mining, ML, AI methods to advance our understanding of effective learning interactions and how to support learners.
- Qualitative Studies for/about L@S (Champion: Amy Ogan) — Studies that take a qualitative or mixed-methods approach to understand learners’ experiences and contextual factors in scaled or scalable learning environments to inform theory and/or design.
- Systems & Tools for Learning (Champion: Chinmay Kulkarni) — Studies that build and evaluate novel systems or tools for learning with designs that are grounded in research on how learning works.
Double-blind Review: Submissions will be reviewed on the basis of originality, research quality, potential impact and value to the development of future learning at scale. 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 instead “in  it is shown”. You should cite your own relevant previous work, so that a reviewer can access it and see the new contributions. The text should not explicitly state that the cited work belongs to the authors.
Synthesis Papers (up to 10 pages) – due Jan 20, 2020
To support collaboration between learning scientists, computer scientists and contributors from other relevant fields, we invite papers that evaluate, synthesize, and contextualize existing bodies of knowledge and research that may be targeted at one or more communities. Such papers may have high value to the community but might not otherwise be accepted only on the basis of original research contributions. Suitable papers include survey papers that provide useful perspectives on major research areas, papers that support or challenge long-held beliefs with compelling evidence, or papers that provide an extensive and realistic evaluation of competing approaches to solving specific problems. Synthesis paper submissions will be reviewed by the full program committee and held to the same standards as research papers, except instead of emphasizing novel research contributions, the emphasis will be on value to the community. Synthesis papers are an area of specific interest for Learning@Scale 2020 and Ido Roll will serve as an area champion this year; you may reach out to him with questions about this submission type.
Work-in-Progress (up to 4 pages) – due Mar 27, 2020
A Work-in-Progress (WiP) concisely summarizes recent findings or other types of innovative or thought-provoking work that has not yet reached a level of completion for a full paper. Topics are the same as 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.
Submission Format: WiP submissions must not exceed 4 pages (including references) and use the CHI Proceedings Format (not Extended Abstract), available in latex and Word. WiP submissions are not anonymous and should therefore include all author names, affiliations and contact information. If accepted, you should prepare a poster to present at the conference venue. Accepted WiP submissions are semi-archival (see details below).
Demonstrations (up to 2 pages) – due Mar 27, 2020
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 to showcase their technologies using this format. A demonstration submission 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.
Submission Format: Demonstration submissions must not exceed 2 pages (including references) and use the CHI Proceedings Format (not Extended Abstract), available in latex and Word. Demonstration submissions are not anonymous and should therefore include all author names, affiliations and contact information.
Workshops (up to 4 pages) – due Feb 3, 2020
Workshops are held on the first day of the conference (March 27) and serve as a gathering place for attendees with shared interests and to build community. A workshop can be half-day or full-day, depending on the goals of the organizers. Workshops can address any Learning @ Scale topic. In your proposal, be clear about the purpose of the workshop, who will benefit from participating, and what participants will be able to do after engaging in the workshop. Specify if the participants need to bring a laptop or other equipment to the workshop. A workshop submission should include the following sections: Background, Organizers, Pre-Workshop Plans, Workshop Structure, Post-Workshop Plans, 250-word Call for Participation, References.
Submission Format: Workshop proposals must not exceed 4 pages (including references) and use the CHI Proceedings Format (not Extended Abstract), available in latex and Word. Demonstration submissions are not anonymous and should therefore include all author names, affiliations and contact information.
Statement on Open Science
Authors are encouraged to conduct their scientific inquiry using emerging best practices in open science. Authors are encouraged to preregister their study design, hypotheses, and analysis plans, and publish these using platforms such as OSF.io or AsPredicted.org. Whenever possible, feasible, and ethical, authors are encouraged to make their data, materials, and scripts openly available for inspection, replication, and follow-up analysis. The best way to share these materials is to use an established platform like OSF.io.
Research and synthesis 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 Computer-Human Interaction (CHI) Conference describes as “semi-archival”, meaning the results reported in the Work-in-Progress paper 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 and conference statistics from prior years are available online.
Research and Synthesis Papers
Jan 20, 2020, 11:59pm HAST – submission deadline
Mar 15, 2020 – notification of acceptance
Apr 18, 2020 – camera-ready final paper due
Feb 3, 2020, 11:59pm HAST – proposal submission deadline
Feb 17, 2020 – notification of acceptance for proposal
Mar 27, 2020, 11:59pm HAST – participant submission deadline
Apr 6, 2020 – notification of acceptance for participants
Work-in-progress and Demonstrations
Mar 27, 2020, 11:59pm HAST – submission deadline
Apr 6, 2020 – notification of acceptance
Apr 18, 2020- camera-ready due
May 27, 2020 – Learning@Scale conference workshops
May 28-29, 2020 – Learning@Scale conference presentations