The Learning at Scale (L@S) conference will be held in 2021 fully online. 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 on these areas. Work from both formal and informal education environments at all levels is encouraged; L@S welcomes studies of higher education and informal adult learning.<\/p>\n\n\n\n
<\/p>\n\n\n\n
About Learning @ Scale<\/strong><\/p>\n\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 massive open online courses, hybrid learning environments combining online and face-to-face, collaborative synchronous and asynchronous learning activities, distributed as mobile and seamless learning applications, intelligent learning support, AI for education. L@S invites examples of learning at scale are invited from the areas of 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\u2019m 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.<\/p>\n\n\n\n Research on learning at scale naturally brings 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.<\/p>\n\n\n\n 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 \u2013 videos watched, forum posts, clicks \u2013 and these outcome proxies. As the field and tools mature, however, we have increasing expectations for new and established measures of learning. <\/p>\n\n\n\n Urgent Challenges and New Opportunities derived from the COVID-19 pandemic<\/strong><\/p>\n\n\n\n This year, the L@S conference is specially interested in research addressing the urgent challenges derived from the COVID-19 pandemic. All learning institutions have been forced to transform and redesign their learning methods, moving from traditional models to hybrid or complete online models at scale. Teachers need best practices and evaluated instructional methods adapted to the new reality as a reference, and technological systems to assure quality education. Students require also guidelines and support for succeeding in these new learning environments as well as coaching and mentoring on learning strategies and self-regulation. All these solutions must also ensure access to equitable quality education towards a more inclusive society, pointed out as one of the key Global Challenges in the new Horizon Europe strategic <\/a>plan. <\/p>\n\n\n\n In this context, and as L@S research expands, we aim for more direct measures of student learning, accompanied by generalizable insight around instructional techniques, learning habits and behaviour change, technological infrastructures, and experimental interventions that improve learning outcomes in the post-COVID-19 decade. Papers presenting ongoing work, including study designs and surveys, behavioral studies, technological solutions, aiming at understanding and discussing how the future of learning at scale will be shaped due to the COVID-19 are especially welcomed this year. Submissions<\/strong><\/p>\n\n\n\n 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<\/a>. 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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n Research Papers <\/strong>(up to 10 pages) \u2013 Abstract due February 15, 2021 (extended), Final submission due February 22, 2021 (extended) <\/p>\n\n\n\n We solicit empirical and theoretical papers on a diverse range of topics relevant to successful learning at scale. For Learning@Scale 2021, we specifically solicit work in five areas of interest to grow our community whilst being inclusive to other work: (1) Intelligence @ scale, (2) Instrucion@ scale, (3) Studies and interventions @ scale, (4) Systems & Tools at scale, and (5) Review and Synthesis papers. 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.<\/p>\n\n\n\n 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 2021 areas of interest are:<\/p>\n\n\n\n Work-in-Progress <\/strong> (up to 4 pages) \u2013 due April 2, 2021 <\/p>\n\n\n\n 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. Demonstrations<\/strong> (up to 2 pages) \u2013 due April 2, 2021<\/p>\n\n\n\n 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:<\/p>\n\n\n\n Workshops <\/strong>(up to 4 pages) \u2013 due February 22, 2021 (extended) <\/p>\n\n\n\n Workshops 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. <\/p>\n\n\n\n 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. <\/p>\n\n\n\n A workshop submission should include the following sections: Background, Organizers, Pre-Workshop Plans, Workshop Structure, Post-Workshop Plans, 250-word Call for Participation, References.<\/p>\n\n\n\n Submission Format:<\/strong> Workshop proposals must not exceed 4 pages (including references) and use the CHI Proceedings Format<\/a>, available in LaTeX, Word, or Overleaf. Workshop submissions are not anonymous and should therefore include all author names, affiliations and contact information. <\/p>\n\n\n\n All submissions will be handled through EasyChair<\/a><\/strong>.<\/p>\n\n\n\n
<\/p>\n\n\n\n
<\/p>\n\n\n\n