Learning Engineering @ Scale

Erin Czerwinski, Jim Goodell, R. Sottilare, Ellen Wagner
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引用次数: 2

Abstract

Scaled learning requires a novel set of practices on the part of professionals developing and delivering systems of scaled learning. IEEE's Industry Connections Industry Consortium for Learning Engineering (ICICLE) defines learning engineering as "a process and practice that applies the learning sciences, using human-centered engineering design methodologies, and data-informed decision-making to support learners and their development." This event will bring together learning engineering experts and other interested conference participants to further define the discipline and strategies to establish learning engineering at scale. It will also serve as a gathering place for attendees with shared interests in learning engineering to build community around the advancement of learning engineering as a professional practice and academic field of study. Interdisciplinary research in the learning, computer and data sciences fields continue to discover techniques for developing increasingly effective technology-mediated learning solutions. However, these applied sciences discoveries have been slow to translate into wide-scale practice. This workshop will bring together conference participants to give input into models for scaling the profession of learning engineering and wide-scale use of learning engineering process and practice models.
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学习工程@ Scale
规模化学习需要专业人员开发和交付规模化学习系统的一系列新颖实践。IEEE的工业连接工业学习工程联盟(ICICLE)将学习工程定义为“应用学习科学的过程和实践,使用以人为中心的工程设计方法和数据知情的决策来支持学习者及其发展。”本次活动将汇集学习工程专家和其他感兴趣的会议参与者,进一步确定大规模建立学习工程的学科和策略。它还将成为对学习工程有共同兴趣的与会者的聚会场所,围绕学习工程作为专业实践和学术研究领域的进步建立社区。学习、计算机和数据科学领域的跨学科研究不断发现开发越来越有效的技术中介学习解决方案的技术。然而,这些应用科学的发现在转化为大规模实践方面进展缓慢。本次研讨会将汇集会议参与者,为扩展学习工程专业和广泛使用学习工程过程和实践模型提供输入。
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Trust, Sustainability and [email protected] L@S'22: Ninth ACM Conference on Learning @ Scale, New York City, NY, USA, June 1 - 3, 2022 L@S'21: Eighth ACM Conference on Learning @ Scale, Virtual Event, Germany, June 22-25, 2021 Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs Evaluating Bayesian Knowledge Tracing for Estimating Learner Proficiency and Guiding Learner Behavior
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