LEADER 03631nam 2200361 450 001 9910571793903321 005 20230829115441.0 035 $a(CKB)5580000000325054 035 $a(NjHacI)995580000000325054 035 $a(EXLCZ)995580000000325054 100 $a20230829d2022 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProceedings of the Ninth ACM Conference on Learning @ Scale /$fRene? F. Kizilcec, Katie Davis, Xavier Ochoa 210 1$aNew York :$cAssociation for Computing Machinery,$d2022. 215 $a1 online resource (14 pages) 311 $a1-4503-9158-3 330 $aIt is our great pleasure to present the Proceedings of the Ninth Annual ACM Conference on Learning at Scale, L@S 2022, held June 1-3, 2022 on Roosevelt Island, New York. 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. 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 this 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. 606 $aInternet in education 615 0$aInternet in education. 676 $a371.3344678 700 $aKizilcec$b Rene? F.$01389350 702 $aDavis$b Katie 702 $aOchoa$b Xavier 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910571793903321 996 $aProceedings of the Ninth ACM Conference on Learning @ Scale$93440788 997 $aUNINA