在计算机学习环境中捕捉学习者互动:设计与应用

Rumana Pathan, Urfa Shaikh, R. Rajendran
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引用次数: 1

摘要

技术增强学习(TEL)环境帮助学习者学习概念丰富的领域。这些学习环境提高了学习者的技能,如决策、权衡分析等,并基于技术支持的教学干预。这样的环境可以帮助学习者解决复杂和结构不良的问题,并提供多种方法来解决问题。然而,初学者经常发现在这样的学习环境中很难做出决定和规范他们学习的内容。为了支持新手学习者,我们需要利用他们与系统的交互来模拟他们的行为。因此,记录学习者与系统的交互是非常重要的。有一些系统的设计和开发是为了提高思维技能,如决策,批判性思维,但是他们不是为了记录学习者的行为而设计的。在本文中,我们开发了一个框架来创建数据记录机制,以捕获用户与此类基于web的系统的交互。我们还将该框架应用到一个名为MEttLE(基于建模的评估学习环境)的基于网络的学习环境中,这是一个旨在教授电气工程背景下评估问题解决的系统。数据记录机制成功地捕获了学习者与系统的所有交互(用户可以在系统中执行509种不同的操作)。此外,我们使用从6个学习器中获得的数据将学习器交互建模为过程模型。该结果与MEttLE开发人员先前报告的一项研究一致,该研究使用通过手动编码学习者与系统交互的屏幕记录获得的定性数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Capturing Learner Interaction in Computer-Based Learning Environment: Design and Application
Technology Enhanced Learning (TEL) environments help learners learn about conceptually rich domains. These learning environments enhance learners' skills such as decision making, trade-off analysis, etc., and is based on pedagogical intervention leveraged by technological affordances. Such environments help learners to solve complex and ill structured problems and provide multiple ways of approaching it. However, novice learner's often find it difficult to take decisions and regulate what they learn in such learning environments. In order to support novice learner's, we need to model their behaviour using their interaction with the system. Hence, logging the learner's interaction with the system is very important. There exists a few systems that are designed and developed to improve thinking skills such as decision making, critical thinking, however they are not designed to log learner behaviour. In this paper we have developed a framework to create data logging mechanism to capture user's interaction with such web-based systems. We have also applied the framework to a webbased learning environment called MEttLE (Modelling-based Estimation Learning Environment) which is a system designed to teach estimation problem solving in the context of electrical engineering. The data logging mechanism successfully captured all the learner's interaction with the system (there were 509 different actions that the user could perform in the system). Additionally, we modelled the learners interaction as a process model using data obtained from 6 learners. The results align with a previous research reported by developers of MEttLE using qualitative data obtained by manually coding screen recordings of learner interaction with the system.
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