［美］李海英1 ［美］陈伯栋2 ［美］珍妮丝•戈贝尔1
( 1 .罗格斯大学教育研究院，美国新不伦瑞克08904, 2.明尼苏达大学课程与教学系，美国明尼阿波利斯55455)
【摘要】规模化学习主要研究大规模、基于技术的、以学习者为中心的学习环境， 而学习环境往往依赖复杂的数据储存、数据传送、数据计算、学习环境界面设计以及智能师生对话设计。2018年6月26日至28日，第五届国际计算机学会（ACM）规模化学习会议（learning at Scale， 简称L@S）在英国伦敦召开， 本届大会的主题为“增强规模化学习的创新” 。大会探讨了新时代下规模化学习和强化学习的创新研究， 开发自动化评估和学生知识模型，提供自动化反馈以及学习干预， 提高规模化学习系统的智适应性。此外， 学者分别就在线学位政策和流程、反馈系统及其自动化发展以及测试试题新类型等三个议题分享了最新研究成果。本文先简要介绍大会总体情况，然后概括分析大会主旨发言、主要议题， 并对会议代表性论文进行评述， 最后论述规模化学习未来的研究发展趋势。
【关键词】规模化学习； 网络学位；慕课；智适应
Innovations in Scaling and Enhancing Learning: Review on the Ffith Annual ACM Conference on Learning at Scale
LI Haiying1, CHEN Bodong2 and Gobert Janice1
(1.Graduate School of Education, Rutgers University, New Brunswick, NJ 08904, USA; 2.Department of Curriculum and Instruction, University of Minnesota, Minneapolis, MN 55455, USA)
Abstract: Learning at Scale (L@S) research is focused on large-scale, technology-based, learner-centered environments, which usually depend on the storage, transmission and computation of complex data, interface design for learning environments, and human-computer conversation/interaction design in intelligent systems. The Fifth Annual ACM Conference on Learning at Scale (L@S) took place in London, UK during June 26-28, 2018. This year’s conference theme was “Innovations in Scaling and Enhancing Learning.” The L@S conference covered innovations in scaling and enhancing learning in emerging contexts, automated assessment and learner knowledge models, automated feedback and interventions, and adaptivity of large-scale learning environments. In addition, L@S researchers shared recent work on policies and logistics of online degrees, automated feedback systems, and emergent types of assessment items. This paper briefl y introduces the L@S conference, reports on emerging paradigms from two keynotes and major research topics covered by full papers. This paper concludes by discussing trends and future directions of the L@S research.
Keywords: Learning at Scale (L@S)；online degree；MOOC；smart adaption
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增强规模化学习的刨新.pdf
