L@S 2023 will be in Copenhagen, Denmark July 20-22. Join us!
Join Us in New York
The 2022 ACM Conference on Learning at Scale will be hosted at the Verizon Executive Education Center located on Cornell Tech’s Roosevelt Island campus adjacent to New York City. You can reach Roosevelt Island by subway, tram, ferry, bike, bus, and car. Discounted hotel rooms are already available at The Graduate Hotel located on Roosevelt Island. For more information on transportation and accommodation, see the Location page.
Masks are a requirement for all indoor events; KN95 masks are available at the registration desk. Attendees are strongly encouraged to perform a COVID-19 rapid test before joining any events.
We will be using the Whova conference platform to enable remote participation: streaming talks, taking questions, poster presentations, discussion, and networking.
You can now find the conference session video recordings on YouTube here.
Blending @ Scale
- 4 workshops
- keynote by President Martha Pollack
- 22 full papers
- 45 works-in-progress and 4 demos
- 2 panels on Funding Learning at Scale and the Future of Assessment
- 2 receptions with poster presentations, and networking
- an engaging online conference experience for all our remote participants
Check out our full organizing committee here. For inquiries about the conference, please email the general chair at email@example.com
Best Paper: Awarded to one (or two) research paper(s) that make an outstanding contribution to our scientific community. Sponsored by Oracle for Research.
Best Undergraduate Paper: Awarded to an outstanding research paper with an undergraduate student as lead or co-author. Sponsored by Schmidt Futures.
Best Dataset: Awarded to a research paper or WIP that shares an especially valuable study dataset to promote open science practices in our community. Sponsored jointly by Oracle for Research and Schmidt Futures.
About Learning at Scale
The Learning at Scale community 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.