Proceedings of the Eighth ACM Conference on Learning @ Scale

Abstract

It is our great pleasure to present the Proceedings of the Eight Annual ACM Conference on Learning at Scale, [email protected] 2021, held virtually on June 22-25, 2021 hosted by the Hasso-Plattner-Institute (HPI), Potsdam, Germany. [email protected] 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. The conference was created by the Association for Computing Machinery (ACM), inspired by the emergence of Massive Open Online Courses (MOOCs) and the accompanying shift in thinking about education. However, the conference has evolved over the years and is now one of the most relevant venues for discussion of the highest quality research on how learning and teaching can be transformed by that diversity of environments. Modern learning at scale typically draws on large amounts of data collected over time from a great variety of learning environments. That data is diverse and heterogeneous, since it is collected from different learning situations. For example, 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. The data is collected through a variety of learning environments enhanced by different technological support that are in constant transformation. 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 or AI for education are examples of these evolving learning at scale environments, which combine innovative teaching and learning models with the latest technologies. Informal environments such as 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 informal communities of learners (e.g. the Explain It Like I'm Five sub-Reddit) are modern large scale environments that the community is also investigating. Research on learning at scale involves dealing with these diversity of data and technology-enhanced environments with a particular purpose: to increase human potential, leveraging data collection, data analysis, human interaction, and varying forms of computational assessment, adaptation and guidance. As the complexity of data and learning environments evolves, the learning at scale research also expands, becoming more sophisticated, interdisciplinary and diverse. As a community, we aim for new methods to measure learning in a more direct way, accompanied by generalizable insight around instructional techniques, learning habits and behaviour change, technological infrastructures, and experimental interventions that will seek to improve learning outcomes in the post-COVID-19 decade. But, creating new methods and measures will not be possible without an interdisciplinary community that brings together learning scientists with computer and data specialists. 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. In addition, we want to emphasize the increasing participation of a diversity of participants and researchers, in particular from the continents of Asia and Europe, contributing to expand the frontiers of the community. The cornerstone of [email protected] is interdisciplinary research and progressive confluence toward more effective and varied future learning worldwide.