Welcome to Learning @ Scale!

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, 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.