Proceedings of the Third (2016) ACM Conference on Learning @ Scale

J. Haywood, V. Aleven, J. Kay, Ido Roll
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This area of research is interdisciplinary, sitting at the intersection of the learning sciences, education, computer science, educational data mining, and learning analytics. \n \n\"Learning at Scale\" refers to new approaches to teaching and learning that involve large numbers of students, thousands or even tens of thousands. It covers face-to-face settings as well as settings in which learners work remotely, whether synchronous or asynchronous. It is concerned with the challenges and affordances of scale: What are innovative forms of learning and instruction that can be orchestrated with very large numbers of learners? Specific topics include, but are not limited to: Pedagogies that enhance learning as scale; personalization and adaptation of learning at scale; selfand co-regulation of learning at scale; platforms, tools, and architectures for learning at scale; usability studies; tools for automated feedback and grading; learning analytics; analysis of log data; studies of application of learning theory; and finally, investigation of student behavior and correlation with learning outcomes, depth and retention of learning, and motivational and affective outcomes. \n \nThe call for papers attracted submissions from all over the world, covering a broad range of topics from the theoretical to the pragmatic. All papers were reviewed according to stringent criteria. Full Papers were reviewed by at least three program committee members, Work-In-Progress Papers and Demo Descriptions by two. 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引用次数: 4

Abstract

It is our great pleasure to present the Proceedings of the Third Annual ACM Conference on Learning at Scale, L@S 2016, held on April 25-26 at the University of Edinburgh, UK, the first time for the conference to be held outside of North America. This conference series is a venue for discussion of the highest quality research on how learning and teaching can be transformed when done at scale. This 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. This area of research is interdisciplinary, sitting at the intersection of the learning sciences, education, computer science, educational data mining, and learning analytics. "Learning at Scale" refers to new approaches to teaching and learning that involve large numbers of students, thousands or even tens of thousands. It covers face-to-face settings as well as settings in which learners work remotely, whether synchronous or asynchronous. It is concerned with the challenges and affordances of scale: What are innovative forms of learning and instruction that can be orchestrated with very large numbers of learners? Specific topics include, but are not limited to: Pedagogies that enhance learning as scale; personalization and adaptation of learning at scale; selfand co-regulation of learning at scale; platforms, tools, and architectures for learning at scale; usability studies; tools for automated feedback and grading; learning analytics; analysis of log data; studies of application of learning theory; and finally, investigation of student behavior and correlation with learning outcomes, depth and retention of learning, and motivational and affective outcomes. The call for papers attracted submissions from all over the world, covering a broad range of topics from the theoretical to the pragmatic. All papers were reviewed according to stringent criteria. Full Papers were reviewed by at least three program committee members, Work-In-Progress Papers and Demo Descriptions by two. Final decisions for acceptance of Full Papers were made by the program committee as a whole, often after extensive discussion of the merits of the paper. Whereas Full Papers present work that is innovative and mature, WiPs and Demos offer a forum for the newest and emerging work at earlier stages, offering pointers to future directions. As such, they fulfill a key role in this fast moving area. An industry session reflects the importance of L@S for the commercial world and for real world deployment. The overall submission numbers did not differ substantially from those of the previous year. Thus, the conference is successfully migrating from the continent of its birth, indicating its international relevance. How could it be different, as Learning at Scale is a truly international phenomenon? We are fortunate to have three outstanding keynote speakers. Sugata Mitra, Professor of Educational Technology and Newcastle University, UK and winner of the 2013 TED Prize presents on "The Future of Learning." Mike Sharples, Chair in Educational Technology, Institute of Educational Technology at The Open University, UK will share insights on "Effective Pedagogy at Scale, Social Learning and Citizen Inquiry." Ken Koedinger, Professor of Human Computer Interaction and Psychology, Director, Pittsburgh Science of Learning Center at Carnegie Mellon University, USA presents the final keynote, shared with the co-located Learning Analytics & Knowledge Conference on the topic, "Practical Learning Research at Scale." Learning@Scale 2016 and edX.org introduce a new interactive format for conference sessions, namely, flipped sessions. Research shows that a flipped format of instruction is effective - in this format, classroom sessions are used for active learning and less interactive forms of learning are done at home. However, most conferences still preserve the decades-old format of mini lectures... Not anymore! Learning@Scale 2016 features flipped conference sessions. Authors of accepted papers, posters, and demos were invited to create online resources, "mini courses," for their contributions, to be hosted by edX. Conference participants familiarize themselves with these resources ahead of the conference. During the session, they discuss the papers and develop relevant themes and ideas based on these papers. After the conference, the platform will remain available for on-going discussions and sharing. The community supported the idea with an overwhelming response.
第三届(2016)ACM Learning @ Scale会议论文集
我们非常高兴地介绍于4月25日至26日在英国爱丁堡大学举行的第三届ACM大规模学习年会(L@S 2016)的会议记录,这是该会议首次在北美以外举行。这个系列会议是一个讨论如何大规模改变学习和教学的最高质量研究的场所。这次会议是由计算机协会(ACM)发起的,灵感来自于大规模开放在线课程(MOOCs)的出现以及随之而来的教育思维转变。这一研究领域是跨学科的,位于学习科学、教育、计算机科学、教育数据挖掘和学习分析的交叉点。“大规模学习”指的是涉及大量学生、数千甚至数万学生的新教学方法。它涵盖了面对面的设置以及学习者远程工作的设置,无论是同步还是异步。它关注的是规模的挑战和支持:什么是创新的学习和教学形式,可以与大量的学习者协调?具体主题包括但不限于:提高学习规模的教学法;大规模学习的个性化与适应性大规模学习的自我调节与协同调节用于大规模学习的平台、工具和架构;可用性研究;用于自动反馈和评分的工具;学习分析;测井资料分析;学习理论的应用研究;最后,调查学生行为与学习成果,学习深度和保留,动机和情感结果的关系。论文征集吸引了来自世界各地的投稿,涵盖了从理论到实用的广泛主题。所有的论文都按照严格的标准进行了审查。论文全文由至少三名项目委员会成员审阅,进行中的论文和演示描述由两名成员审阅。接受论文全文的最终决定是由项目委员会作为一个整体做出的,通常是在对论文的优点进行广泛讨论之后。全文论文展示的是创新和成熟的工作,而WiPs和Demos为早期阶段的最新和新兴工作提供了一个论坛,为未来的方向提供了指引。因此,他们在这个快速发展的地区发挥着关键作用。一个行业会议反映了L@S对于商业世界和现实世界部署的重要性。总的提交数量与前一年相比没有太大差异。因此,会议正成功地从其诞生的大陆移出,表明其具有国际意义。既然大规模学习是一种真正的国际现象,它又有什么不同呢?我们有幸邀请到三位杰出的主讲人。苏加塔·米特拉,英国纽卡斯尔大学教育技术教授,2013年TED大奖得主,演讲主题为“学习的未来”。英国开放大学教育技术研究所教育技术主席Mike Sharples将分享“大规模有效教学法、社会学习和公民探究”的见解。美国卡耐基梅隆大学匹兹堡学习科学中心主任、人机交互与心理学教授Ken Koedinger在学习分析与知识会议上发表了最后的主题演讲,主题是“大规模的实践学习研究”。Learning@Scale 2016和edX.org为会议引入了一种新的互动形式,即翻转会议。研究表明,翻转教学模式是有效的——在这种模式下,课堂教学用于主动学习,而较少互动的学习形式则在家里完成。然而,大多数会议仍然保留着几十年前的迷你讲座形式……不了!Learning@Scale 2016年特色翻转会议。被接受的论文、海报和演示的作者被邀请创建在线资源“迷你课程”,以供他们的贡献,由edX主持。会议参与者在会议前熟悉这些资源。在会议期间,他们讨论论文,并根据这些论文发展相关的主题和想法。会议结束后,该平台将继续进行讨论和分享。社区以压倒性的反应支持这个想法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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