L@S'20 : proceedings of the Seventh ACM Conference on Learning @ Scale : August 12-14, 2020, Virtual Event, USA / / general chair, David Joyner ; program chairs, Rene Kizilcec, Susan Singer

• Pubblicazione: New York : , : Association for Computing Machinery, , 2020
• Descrizione fisica: 1 online resource (442 pages) : illustrations
• Disciplina: 371.334
• Soggetto topico: Distance education - Computer-assisted instruction; Internet in education; MOOCs (Web-based instruction); Open learning
• Persona (resp. second.): JoynerDavid; KizilcecRene; SingerSusan
• Note generali: Includes index.
• Sommario/riassunto: It is our great pleasure to present the Proceedings of the Seventh Annual ACM Conference on Learning at Scale, L@S 2020, held virtually on August 12-14, 2020.Learning at Scale investigates large-scale, technology-mediated learning environments. 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. While the conference was originally inspired by the emergence of MOOCs, large-scale technology mediated learning environments are very diverse and the conference series is a venue for discussion of the highest quality research on how learning and teaching can be transformed by that diversity of environments. Intelligent tutoring systems, open learning courseware, learning games, citizen science communities, collaborative programming communities, community tutorial systems, and the countless informal communities of learners are all examples of learning at scale. These systems either depend upon large numbers of learners, or they are enriched through use of data generated by the previous use of many learners. They share a common purpose--to increase human potential--and a common infrastructure of data and computation to enable learning at scale.Investigations of learning at scale naturally bring together two different research communities. Learning scientists are drawn to these innovative environments to study established and emerging forms of knowledge production, transfer, modeling, and co-creation. Computer scientists are drawn to the field as a powerful site for the development and application of advanced computational techniques. At its very best, the Learning at Scale community supports the interdisciplinary investigation of these important sites of learning and human development.The goal of the Learning at Scale community is the understanding and enhancement of human learning. In emerging education technology genres, researchers often use a variety of proxy measures for learning, including measures of participation, persistence, completion, satisfaction, and activity. In the early stages of investigating a technological genre, it is entirely appropriate to begin lines of research by investigating these proxy outcomes. As lines of research mature, however, it is important for the community of researchers to hold each other to increasingly high standards and expectations for directly investigating thoughtfully constructed measures of learning. In the early days of research on MOOCs, many researchers documented correlations between measures of activity (videos watched, forums posted, clicks) and outcome proxies including participation, persistence, and completion. As learning at scale research matures, studies that document these kinds of correlations are giving way to studies of student learning that produce evidence of instructional techniques, technological infrastructures, learning habits, and experimental interventions that improve learning.In this year's call for papers, we solicited work in five areas of interest to grow our community whilst being inclusive to other work. Each area was represented by a community champion to answer questions about potential submissions and ensure high-quality reviewing in the area: Causal Inference at Scale (Champion: Thomas Staubitz) -- Studies that use digital learning environments with experimental designs to investigate factors that increase learning and refine theories by, for example, identifying sources of heterogeneity. Learning and Curriculum Analytics (Champion: Zach Pardos) -- Studies that analyze large educational datasets with data mining, ML, AI methods to advance our understanding of effective learning interactions and how to support learners. Qualitative Studies for/about L@S (Champion: Amy Ogan) -- Studies that take a qualitative or mixed-methods approach to understand learners' experiences and contextual factors in scaled or scalable learning environments to inform theory and/or design. Systems & Tools for Learning (Champion: Chinmay Kulkarni) -- Studies that build and evaluate novel systems or tools for learning with designs that are grounded in research on how learning works. Synthesis Papers (Champion: Ido Roll)-- Studies that evaluate, synthesize, and contextualize existing bodies of knowledge and research to support collaboration.The call for papers attracted submissions from all over the world, covering a broad range of topics from the theoretical to the pragmatic. We received submissions on a variety of topics including: novel assessments of learning, drawing on computational techniques for automated, peer, or human-assisted assessment; new methods for validating inferences about human learning from established measures, assessments, or proxies; experimental interventions in large-scale learning environments that show evidence of improved learning outcomes; domain independent interventions inspired by social psychology, behavioral economics, and related fields with the potential to benefit learners in diverse fields and disciplines; domain specific interventions inspired by discipline-based educational research that have the potential to advance teaching and learning of specific ideas, misconceptions, and theories within a field; tools or techniques for personalization and adaptation, based on log data, user modeling, or choice; usability studies and effectiveness studies of design elements for students or instructors; tools and pedagogy to promote community, support learning, or increase retention in atscale environments; new tools and techniques for learning at scale; best practices in the archiving and reuse of learner data in safe, ethical ways; fairness in student predictive models; challenges in learning at scale across the globe; exploration of affordable degrees at scale; innovations in platforms for supporting learning at scale; and platforms to broadly engage individuals of all ages in citizen science. In all topics, we encourage the use of best practices in open science, including pre-planning and preregistration as well as a particular focus on contexts and populations that have been historically not well served.Our keynote speaker, Katie Davis shared insights from her recently published book, co-authored with Dr. Cecilia Aragon, titled Writers in the Secret Garden: Fanfiction, Youth, and New Forms of Mentoring in a talk titled -- What My Little Pony Can Teach Us About Interest-Driven Learning. Our keynote panel with Candace Thille (moderator), Ellen Wagner, Stephanie Teasley, Sidney D'Mello explored the ethics of learning at scale. The addition of workshops was well received this year with eight contributions. We also had glimpses into emerging work as 39 contributors shared their work in progress (WiPs) and demonstrations of their work.The COVID-19 pandemic and global attention to Black Lives Matter in response to George Floyd's killing have laid bare the equity gaps in education and generational racial inequities. Learning at Scale strives to be part of the solution and we have much room to improve. The work of our community is needed now more than ever. We added a call for abstracts on the impact of COVID-19 on virtual learning to the 2020 program. We commit to doing more to being inclusive in our programs, in terms of presenters, participants and work presented.
• Titolo autorizzato: L@S'20
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910412319903321
• Lo trovi qui: Univ. Federico II