{"id":26,"date":"2025-11-26T16:43:49","date_gmt":"2025-11-26T16:43:49","guid":{"rendered":"https:\/\/learningatscale.hosting.acm.org\/las2026\/?page_id=26"},"modified":"2025-12-12T00:16:07","modified_gmt":"2025-12-12T00:16:07","slug":"call-for-papers","status":"publish","type":"page","link":"https:\/\/learningatscale.hosting.acm.org\/las2026\/call-for-papers\/","title":{"rendered":"Call for Papers"},"content":{"rendered":"
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Call for Papers<\/strong><\/h1>\n

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 online courses, mobile learning applications, intelligent tutoring systems, open courseware, learning games, citizen science communities, collaborative programming communities, community tutorial systems, shared critique communities, informal communities of learners, and curricular and administrative systems that support learner pathfinding 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.<\/span><\/p>\n

Research on learning at scale naturally brings together several 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, modeling, and interaction. The cornerstone of L@S is interdisciplinary research and progressive confluence toward more effective and varied future learning.<\/span><\/p>\n

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Submissions<\/span><\/h1>\n

The ACM Learning@Scale conference solicits original research paper submissions on methodologies, case studies, qualitative and quantitative analyses, tools, or technologies that can contribute to learning at scale, broadly construed. Three kinds of contributions will be accepted: Research Papers, Work-in-Progress, and Demonstrations. Accepted works must be presented at the conference and will be included in the proceedings (more details about these contributions can be found below).<\/span><\/p>\n

Paper submissions, reviewing and notification to authors will be handled using the conference page at <\/span>EasyChair<\/b> (<\/span>https:\/\/easychair.org\/conferences\/?conf=las2026<\/b><\/a>). Submissions must be in PDF format, anonymized for double-blind review (see below), follow the <\/span>ACM 2-column proceedings template (<\/b>Word<\/b><\/a> or <\/b>Latex<\/b><\/a>)<\/b>, written in English, contain original work, and not be under review for any other venue while under review for this conference. <\/span>The page limits for the different submissions exclude the pages used for references. <\/b>For research papers, the abstract must be submitted before the final contribution. <\/span>The length of the abstract should not exceed 350 words.<\/b><\/p>\n

Anonymization policy for double-blind review<\/i><\/b>: Submissions will be reviewed on the basis of <\/span>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. All submissions, with the exception of workshop proposals (see below), should be anonymous. Thus, papers submitted for review <\/span>MUST NOT<\/b> contain the authors\u2019 names, affiliations, or any information that may disclose the authors\u2019 identity (this includes names or logos of labs\/centers, tools, grant numbers, and acknowledgments). Identifying 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 self-references, such as \u201cin [1] we show\u201d \u2014 consider instead \u201cin [1] it is shown\u201d or \u201cin [1] Smith et al. show …\u201d (citing yourself in the third person just like how you would cite other researchers). You should definitely cite your own relevant previous work, so that a reviewer can access it and see the new contributions. However, the text should not explicitly state that the cited work belongs to the authors (i.e., do not use \u201cAuthor (year)\u201d to anonymize). Authors are permitted to post their work to public archives (e.g., arXiv, EdArXiv). Reviewers will be asked not to check archives during the review process.
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<\/span>Statement on Open Science<\/i><\/b>: Authors are encouraged to conduct their scientific inquiry using emerging best practices in open science. Authors are encouraged to pre-register their study design, hypotheses, and analysis plans, and publish these using platforms such as OSF.io (<\/span>https:\/\/osf.io\/<\/span><\/a>) or AsPredicted.org (<\/span>https:\/\/aspredicted.org\/<\/span><\/a>). 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 <\/span>OSF.io<\/span><\/a>.\u00a0 When you submit your paper, there will be three questions in the submission form where you can say if any part of our paper is pre-registered, gives out data, or gives out your materials.\u00a0\u00a0<\/span><\/p>\n

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Research Papers<\/b><\/h2>\n

Up to 10 pages (not including references). We recommend keeping the length of the papers proportional to the size and the scope of the research contribution. All appendices and supporting materials should be published in an online repository (e.g., OSF.io) and referenced in the main article. Please make sure that the materials adhere to blind-reviewing standards (e.g., use the \u201canonymous link\u201d function on OSF.io).<\/span><\/i><\/span><\/p>\n

Abstract due – <\/span>February 9, 2026<\/b> (11:59 PM AoE)<\/span><\/p>\n

Papers due – <\/span>February 16, 2026<\/b> (11:59 PM AoE)<\/span><\/span><\/p>\n

Learning@Scale 2026 solicits empirical and theoretical papers on, but not limited to, the following topics (in no particular order):\u00a0<\/span><\/p>\n