{"id":9,"date":"2026-10-30T16:09:24","date_gmt":"2026-10-30T16:09:24","guid":{"rendered":"https:\/\/learningatscale.hosting.acm.org\/las2027\/call-for-papers\/"},"modified":"2026-11-04T15:44:49","modified_gmt":"2026-11-04T15:44:49","slug":"call-for-papers","status":"publish","type":"page","link":"https:\/\/learningatscale.hosting.acm.org\/las2027\/call-for-papers\/","title":{"rendered":"Call for Papers"},"content":{"rendered":"\n
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CONTRIBUTE TO L@S 2027<\/p>\n\n\n

Call for Papers<\/h1>\n\n\n

Original research, emerging ideas, and hands-on demonstrations that advance learning at scale.<\/p>\n\n<\/div>\n\n\n

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Submit via EasyChair<\/a><\/div>\n\n<\/div>\n\n\n

On this page: Deadlines<\/a> \u00b7 Contributions<\/a> \u00b7 Topics<\/a> \u00b7 Submission guidelines<\/a> \u00b7 Review process<\/a> \u00b7 Publication<\/a><\/p>\n\n\n

Important dates<\/h2>\n\n\n

All deadlines are final. No extensions.<\/strong> Times are 11:59 PM Anywhere on Earth (AoE).<\/p>\n\n\n

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Research Papers & WiP<\/h3>\n\n\n
Milestone<\/th>Deadline<\/th><\/tr><\/thead>
Abstracts (Research Papers & WiP)<\/td>January 22, 2027<\/td><\/tr>
Papers (Research Papers & WiP)<\/td>January 29, 2027<\/td><\/tr>
Author notification<\/td>March 31, 2027<\/td><\/tr>
Camera-ready version<\/td>May 5, 2027<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<\/div>\n\n\n
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Demonstrations<\/h3>\n\n\n
Milestone<\/th>Deadline<\/th><\/tr><\/thead>
Demonstration paper<\/td>April 14, 2027<\/td><\/tr>
Author notification<\/td>April 28, 2027<\/td><\/tr>
Camera-ready version<\/td>May 8, 2027<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<\/div>\n\n<\/div>\n\n\n

Abstracts are required for both Research Papers and Work-in-Progress, with a maximum of 350 words. Demonstrations do not require a separate abstract submission.<\/p>\n\n<\/div>\n\n\n

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Three ways to contribute<\/h2>\n\n\n
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Research Papers<\/h3>\n\n\n

UP TO 10 PAGES<\/p>\n\n\n

Empirical or theoretical work that advances learning at scale. Keep the length proportional to the contribution.<\/p>\n\n<\/div>\n\n<\/div>\n\n\n

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Work-in-Progress<\/h3>\n\n\n

UP TO 4 PAGES<\/p>\n\n\n

Recent findings and innovative work still in development. Accepted WiP submissions are presented as posters.<\/p>\n\n<\/div>\n\n<\/div>\n\n\n

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Demonstrations<\/h3>\n\n\n

UP TO 2 PAGES<\/p>\n\n\n

Interactive, hands-on presentations of technologies and environments relevant to learning at scale.<\/p>\n\n<\/div>\n\n<\/div>\n\n<\/div>\n\n\n

Page limits exclude references. Appendices and supporting materials must be hosted in an online repository and referenced in the paper, using links that preserve anonymous review.<\/p>\n\n\n

Demonstration proposals<\/h3>\n\n\n

Describe both the merit and nature of the technology and how visitors will interact with the live demonstration. If associated with a Research Paper or WiP submission, cite that work in the two-page proposal. Authors of accepted papers are encouraged to demonstrate their technologies.<\/p>\n\n\n

Workshops and tutorials<\/h3>\n\n\n

Workshops and tutorials are coordinated by Festival of Learning 2027<\/a>. Please consult the Festival website for calls and details.<\/p>\n\n<\/div>\n\n\n

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Research scope & topics<\/h2>\n\n\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.<\/p>\n\n\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.<\/p>\n\n\n

We invite methodologies, case studies, qualitative and quantitative analyses, tools, and technologies. Topics include, but are not limited to:<\/p>\n\n\n