Call for Papers: Learning @ Scale 2018, June 26-28, London, UK

Call for Papers: Learning @ Scale 2018, June 26-28, London, UK

We are excited to announce that the Learning at Scale (L@S) conference will be held June 26-28, 2018 in London UK. Learning at Scale seeks 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.

The conference will be part of a collocated, weeklong Festival of Learning. As part of the Festival of Learning, L@S 2018 will be preceded by the 2018 International Conference of the Learning Sciences and followed by the 2018 Artificial Intelligence in Education Conference. The weeklong Festival of Learning will be June 24-30. The L@S conference will be June 26-28, with a common festival day on June 27, and the single-track L@S events on June 26 and 28.

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Paper Submissions | Open Science | Archival Proceedings | Important Dates

Paper Submissions

We solicit paper submissions reporting on rigorous research on methodologies, studies, analyses, tools, or technologies for learning at scale. We accept four kinds of papers, detailed below: Full Research Papers, Full Synthesis Papers, Work-in-Progress Posters, and Demonstrations. Accepted papers and posters are presented at the conference, and included in the proceedings.
In addition, accepted authors will have the option of presenting supplementary online materials to aid in their presentation. Accepted authors are encouraged to consider using their allotted conference time for activities or discussion in addition to delivering presentations or showing posters. We are also encouraging best practices in open science as described in the Statement on Open Science below.

Full Research Papers - Due Jan 21, 2018

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.

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". 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.

Full Context/Synthesis Papers - Due Jan 21, 2018

One of the purposes of the Learning@Scale conference and community is to create a context for communities of learning scientists and computer scientists to engage in meaningful discourse and start fruitful collaborations. In that spirit we invite papers that evaluate, synthesize, and contextualize existing bodies of knowledge and research targeted at one or both communities. These papers will provide a high value to our community but would otherwise not be accepted because they lack novel 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. Submissions will be reviewed by the full PC and held to the same standards as traditional research papers except instead of emphasizing novel research contributions, the emphasis will be on value to the community. Accepted papers will be presented at the conference and included in the proceedings.

Work-in-Progress - Due Mar 24, 2018

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.

Formatting: Work-in-Progress submissions 4 pages or fewer and must use the ACM CHI Archive Format, available in latex and Word 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.

Demonstrations - Due Mar 24, 2018

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 and must use the ACM CHI Archive Format, available in latex and Word , 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.

Statement on Open Science

Authors are strongly encouraged to conduct their scientific inquiry using emerging best practices from open science.

  • Preregistration. Researchers are encouraged to pre-register their designs, hypotheses, and analysis plans and publish these with the Open Science Foundation or a similar organization (see, for example ). When presenting the results from pre-registered studies, authors are encouraged to report pre-registered and exploratory analyses separately (e.g., see Gehlbach et al. (2015) Creating Birds of Similar Feathers ). Causal claims from experimental interventions are strongest when researchers constrain their analytic space through pre-planning and pre-registration -- see Tukey (1980) We Need Exploratory and Confirmatory Authors  and Gelman and Loken (2013) The Garden of Forking Paths. Best practices in open science pre-planning and pre-registration are not well-established, but experimentalists in the Learning at Scale community are encouraged to explore and develop these practices.
  • Open Data and Replication. As studies are conducted, whenever possible, feasible, and ethical, researchers are encouraged to make their data and analytics openly available for inspection, replication, and additional analysis. Providing your own web access is fine, but can be hard to maintain, so use of general web-based sharing resources is encouraged (see for example LearnSphere). We encourage papers that test external validity through so-called replication studies. Reviewers will be instructed to evaluate such papers both for the open external validity issues the study addresses (e.g., through use of different student populations, different content or context, etc.) and for the importance and influence of the original work being replicated.

Archival Proceedings

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 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 from prior years can be accessed here:

AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of your conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference.)

Important Dates

Full papers (research and context/synthesis)

  • Jan 21, 2018, 11:59pm HAST - submission deadline
  • Feb 26, 2018 - notification of acceptance
  • Apr 19, 2018 - camera-ready due

Work-in-progress and demo papers

  • Mar 24, 2018, 11:59pm HAST - submission deadline
  • Apr 19, 2017 - notification of acceptance
  • May 1, 2017 - camera-ready due


June 26-28, 2017 - Learning@Scale Conference

Contact us at: LEARNINGATSCALE-CC (at) acm (dot) org