学习分析和机器学习

D. Gašević, C. Rosé, George Siemens, A. Wolff, Z. Zdráhal
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引用次数: 24

摘要

学习分析(LA)作为一个领域仍处于起步阶段。现在从业人员中突出的许多技术来自不同的领域,包括HCI、统计学、计算机科学和学习科学。为了使洛杉矶分析作为一门学科发展和进步,必须应对两个重大挑战:1)发展分析方法和技术,这些方法和技术是洛杉矶分析学科的原生,2)洛杉矶的从业者开发反映分析的社会和计算维度的算法和模型。本次研讨会向学习分析领域的研究人员介绍了机器学习(ML),以及ML在构建下一代分析模型方面可以提供的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning analytics and machine learning
Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.
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