第二届(2015)ACM Learning @ Scale会议论文集

G. Kiczales, D. Russell, B. Woolf
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引用次数: 5

摘要

我们非常高兴地欢迎您参加2015年ACM大规模学习会议。今年是会议的第二年,我们看到提交给会议的文件数量有了显著的增长,文章的质量也有了全面的提高。今年的会议延续了作为展示研究成果和内幕故事的主要论坛的传统,这些故事是关于是什么使在线教育系统大规模运作的。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。项目委员会审查并接受了以下内容:地点或轨道审查已接受的完整技术论文90篇23篇25%短技术论文12篇5篇41%正在进行的论文54篇47篇80%由于会议仍处于形成阶段,我们接受了所有正在进行的工作的很大一部分,因为我们发现阅读它们的经验非常宝贵。我们仍然是一个新生的领域,学习最新的工作反映了我们所知道的真实的快速变化的本质。我们鼓励与会者同时参加两个主题演讲。这些有价值和有见地的演讲可以并将引导我们更好地理解我们领域的未来:通过启发学习和生成适应在全国范围内实现96%的掌握,Zoran Popovic(华盛顿大学),大规模学习的机器学习,Peter Norvig (b谷歌)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proceedings of the Second (2015) ACM Conference on Learning @ Scale
It is our great pleasure to welcome you to ACM conference Learning at Scale 2015. In this, the second year of the conference, we have seen a significant growth in the number of submissions to the conference and an overall improvement in the quality of the contributions. This year's conference continues the tradition of being the premier forum for presentation of research results and inside stories about what makes online educational systems operate at scale. The call for papers attracted submissions from all over the world, covering a broad range of topics from the theoretical to the pragmatic. The program committee reviewed and accepted the following: Venue or Track Reviewed Accepted Full Technical Papers 90 23 25% Short Technical Papers 12 5 41% Work in Progress Papers 54 47 80% Since the conference is still in its formative years, we accepted a large fraction of all the Works in Progress because we found the experience of reading through them to be so valuable. We are still a nascent field, and learning about the very latest work reflects the rapidly changing nature of what we know to be true. We encourage attendees to attend both keynotes. These valuable and insightful talks can and will guide us to a better understanding of the future of our field: Achieving 96% mastery at national scale through inspired learning and generative adaptivity, Zoran Popovic (University of Washington) Machine Learning for Learning at Scale, Peter Norvig (Google)
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