Operationalizing and Detecting Disengagement Within Online Science Microworlds
Operationalizing and Detecting Disengagement Within Online Science Microworlds
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DOI：
10.1080/00461520.2014.999919
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发表时间：
2015-01-02
影响因子：
8.8
通讯作者：
Wixon, Michael B.
中科院分区：
文献类型：
--
作者：
Gobert, Janice D.;Baker, Ryan S.;Wixon, Michael B.
近年来，人们对学习过程中的参与越来越感兴趣。这在科学、技术、工程和数学领域尤其令人感兴趣，许多学生在这些领域奋斗，美国需要技术工人。本文列出了一些重要的问题，框架研究这一主题，并提供了一个审查现有的一些工作，在科学学习的参与类似的目标。具体来说，在这里，我们寻求帮助更好地具体化参与，一个模糊的结构，通过操作和检测（即，使用计算方法识别）与参与相反的脱离行为。反过来，我们描述我们的实时检测器（即，机器学习模型）的脱离行为，以及它是如何发展的。最后，我们解决我们正在进行的研究，我们的脱离行为的检测器将被用来干预在真实的时间，以更好地支持学生的科学探究学习在Inq-ITS（查询智能辅导系统; Gobert，圣佩德罗，贝克，托托，和蒙塔尔沃，2012年; Gobert，圣佩德罗，Raziuddin，和贝克，2013年）。
In recent years, there has been increased interest in engagement during learning. This is of particular interest in the science, technology, engineering, and mathematics domains, in which many students struggle and where the United States needs skilled workers. This article lays out some issues important for framing research on this topic and provides a review of some existing work with similar goals on engagement in science learning. Specifically, here we seek to help better concretize engagement, a fuzzy construct, by operationalizing and detecting (i.e., identifying using a computational method) disengaged behaviors that are antithetical to engagement. We, in turn, describe our real-time detector (i.e., machine learned model) of disengaged behavior and how it was developed. Last, we address our ongoing research on how our detector of disengaged behavior will be used to intervene in real time to better support students' science inquiry learning in Inq-ITS (Inquiry-Intelligent Tutoring System; Gobert, Sao Pedro, Baker, Toto, & Montalvo, 2012; Gobert, Sao Pedro, Raziuddin, & Baker, 2013).