Explanatory learner models: Why machine learning (alone) is not the answer
Explanatory learner models: Why machine learning (alone) is not the answer
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解释性学习者模型：为什么机器学习（单独）不是答案
DOI：
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发表时间：
2019
影响因子：
6.6
通讯作者：
K. Koedinger
中科院分区：
文献类型：
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作者：
C. Rosé;Elizabeth Mclaughlin;Ran Liu;K. Koedinger
利用数据来理解学习和改善教育前景广阔。然而，人工智能和机器学习研究人员开发更准确地预测标记数据的创新模型，并不能简单地实现这一承诺。随着人工智能的进步，建模技术及其生成的模型变得越来越复杂，通常涉及数万个或更多参数。尽管核心机器学习社区在解释复杂模型方面取得了长足的进步，但在这些“黑匣子”建模的情况下，研究团队可能几乎没有机会深入了解这些模型是如何运作的，为什么，甚至当这些模型被应用到它们所建立的数据之外时，这些模型是否会起作用。与其仅仅依靠人工智能专业知识，我们建议学习工程团队引入跨学科的专业知识来开发解释性学习者模型，除了提供准确的预测外，还提供可解释和可操作的见解。我们将举例说明在不同的课程内容（如数学和写作）和不同的目标（如改进学生模型和生成可操作的反馈）中使用不同类型的数据（如点击流和话语数据）。我们建议学习型工程团队、共享基础设施和资金激励，以更好地开发解释性学习者模型，从而推进学习科学，产生更好的教学实践，并明显改善学生的学习。从业者笔记：关于这个主题的已知内容学习分析和教育数据挖掘的研究人员已经成功地创建了优化预测的创新数据模型。其中一些模型产生了科学或实用的见解，但很少有模型被投入使用，并被证明能提高学生的学习水平。我们提供了学习器解释模型发展的例子，这些模型不仅准确地预测数据，而且提供了科学的见解并产生了实际的结果。特别是，具有认知科学和数学教育内容专业知识的研究人员使用基于人工智能的数据分析来发现以前未被认识到的几何学生学习障碍。他们使用模型衍生的见解来重新设计在线辅导系统和“闭环”，通过实验证明新系统比原来的系统产生更好的学生学习。对实践和/或政策的启示我们定义了解释性学习模型，并提供了生成这些模型的过程的表述，这些模型涉及跨学科团队，采用人机交互和学习工程方法。根据我们的经验，我们建议学习型工程团队、共享基础设施和资金激励，以更好地开发解释性学习者模型，从而推进学习科学，产生更好的教学实践，并明显改善学生的学习。[摘自作者]
Using data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving tens of thousands of parameters or more. Though strides towards interpretation of complex models are being made in core machine learning communities, it remains true in these cases of "black box" modeling that research teams may have little possibility to peer inside to try understand how, why, or even whether such models will work when applied beyond the data on which they were built. Rather than relying on AI expertise alone, we suggest that learning engineering teams bring interdisciplinary expertise to bear to develop explanatory learner models that provide interpretable and actionable insights in addition to accurate prediction. We describe examples that illustrate use of different kinds of data (eg, click stream and discourse data) in different course content (eg, math and writing) and toward different goals (eg, improving student models and generating actionable feedback). We recommend learning engineering teams, shared infrastructure and funder incentives toward better explanatory learner model development that advances learning science, produces better pedagogical practices and demonstrably improves student learning. Practitioner NotesWhat is already known about this topic Researchers in learning analytics and educational data mining have been successful in creating innovative models of data that optimize prediction.Some of these models produce scientific or practical insights and fewer have been put into use and demonstrated to enhance student learning.What this paper adds We provide examples of development of explanatory models of learners that not only accurately predict data but also provide scientific insights and yield practical outcomes.In particular, researchers with expertise in cognitive science and math education content use AI‐based data analytics to discover previously unrecognized barriers to geometry student learning. They use model‐derived insights to redesign an online tutoring system and "close‐the‐loop" by experimentally demonstrating that the new system produces better student learning than the original.Implications for practice and/or policy We define explanatory learning models and provide an articulation of a process for generating them that involves interdisciplinary teams employing human–computer interaction and learning engineering methods.Based on our experiences, we recommend learning engineering teams, shared infrastructure and funder incentives toward better explanatory learner model development that advances learning science, produces better pedagogical practices and demonstrably improves student learning. [ABSTRACT FROM AUTHOR]
通过社区范围的审议支持虚拟团队的组建
影响因子：
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作者：
Wen, Miaomiao;Maki, Keith;Dow, Steven;Herbsleb, James D.;Rose, Carolyn
通讯作者：
Rose, Carolyn
DOI：
10.18653/v1/p19-1575
发表时间：
2019-07
期刊：
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影响因子：
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作者：
James Fiacco;Samridhi Choudhary;C. Rosé
通讯作者：
James Fiacco;Samridhi Choudhary;C. Rosé