Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity
Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity
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DOI：
10.1007/s40593-016-0131-y
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发表时间：
2017-06-01
影响因子：
4.9
通讯作者：
Roll, Ido
中科院分区：
文献类型：
--
作者：
Fratamico, Lauren;Conati, Cristina;Roll, Ido
交互式模拟可以促进探究性学习。然而，类似于其他探索性学习环境，学生可能并不总是有效地学习在这些非结构化的环境。因此，提供自适应支持具有很大的潜力，可以帮助通过这些丰富的活动改善学生的学习。提供适应性支持需要一个既能评估学习又能提供相关反馈的学生模型。构建这样一个交互式模拟模型尤其具有挑战性，因为交互的探索性质使得很难先验地知道哪些行为有利于学习。为了解决这个问题，在本文中，我们利用学生建模框架（Kardan和Conati，2011年），专门解决在交互式模拟建模学生的挑战。该框架已经成功地应用于建立一个学生模型，并给出自适应干预的约束满意度的交互式模拟。我们试图调查的一般性框架，通过建立学生模型，更复杂的模拟电路称为电路建设工具包（CCK）。我们评估的替代表示与CCK记录的交互数据，捕获不同数量的粒度和功能工程。然后，我们应用（Kardan和Conati，2011）中提出的学生建模框架，根据学生的交互行为对学生进行分组，将这些行为映射到学习成果中，并利用产生的集群对新学习者进行分类。从100名大学生的CCK模拟工作收集的数据表明，所提出的框架是能够成功地将学生分组的高，低学习者和识别模式的生产行为是常见的跨表示，可以通知实时反馈。除了呈现这些结果，我们讨论了测试的交互表示的粒度和功能工程的水平之间的权衡，他们的能力来评估学习，学生分类，并告知反馈。
Interactive simulations can facilitate inquiry learning. However, similarly to other Exploratory Learning Environments, students may not always learn effectively in these unstructured environments. Thus, providing adaptive support has great potential to help improve student learning with these rich activities. Providing adaptive support requires a student model that can both evaluate learning as well inform relevant feedback. Building such a model for interactive simulations is especially challenging because the exploratory nature of the interaction makes it hard to know a priori which behaviors are conducive to learning. To address this problem, in this paper we leverage the student modeling framework proposed in (Kardan and Conati, 2011) to specifically address the challenge of modeling students in interactive simulations. The framework has already been successfully applied to build a student model and to give adaptive interventions for an interactive simulation for constraint satisfaction. We seek to investigate the generality of the framework by building student models for a more complex simulation on electric circuits called Circuit Construction Kit (CCK). We evaluate alternative representations of logged interaction data with CCK, capturing different amounts of granularity and feature engineering. We then apply the student modeling framework proposed in (Kardan and Conati, 2011) to group students based on their interaction behaviors, map these behaviors into learning outcomes and leverage the resulting clusters to classify new learners. Data collected from 100 college students working with the CCK simulation indicates that the proposed framework is able to successfully classify students in groups of high and low learners and identify patterns of productive behaviors that are common across representations that can inform real-time feedback. In addition to presenting these results, we discuss trade-offs between levels of granularity and feature engineering in the tested interaction representations in terms of their ability to evaluate learning, classify students, and inform feedback.