Empirical Research: Emerging Research: Using Automated Detectors to Examine the Relationships Between Learner Attributes and Behaviors During Inquiry in Science Microworlds

实证研究：新兴研究：使用自动检测器检查科学微观世界探究过程中学习者属性和行为之间的关系

• 批准号: 1008649
• 负责人: Janice Gobert
• 金额: $ 98.61万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1008649&HistoricalAwards=false
• 关键词: Empirical; Research; Emerging; Using; Automated

The research study will examine the relationship between student attributes and behavior while working through a series of science projects on computer. This project seeks to advance understanding of how middle school student behaviors within using educational software influence science learning. The investigators will measure how student attributes such as poor goal orientation, low self-efficacy, and low perseverance are associated with learning outcomes. They will investigate how these attributes lead to negative behaviors such as gaming the system rather than learning the material, off-task behavior, haphazard inquiry, and help avoidance. The study will use survey measures of student attributes, automated detectors of student behavior during use of educational software, and measures of student learning to develop models of how different student behaviors lead to improved learning outcomes. The models are chosen to test which behaviors mediate the relationships between student attributes and learning and how the behaviors do so. The researchers will use statistical models, such as path analysis, to test their hypotheses. The study will be conducted in 500 ethnically and economically diverse middle school students who are part of a science assessment project in those schools. The findings are intended to serve as a basis for creating new educational software and classroom practices that identify in real-time students with low grit, low self-efficacy, and non-productive goal orientation. Through research with diverse populations, and in a domain where many American students are falling behind, the project has the potential to benefit learners currently disenfranchised from science learning.

这项研究将通过一系列的计算机科学项目来研究学生的属性和行为之间的关系。该项目旨在促进对中学生在使用教育软件时的行为如何影响科学学习的理解。研究人员将测量学生的不良目标定向、低自我效能感和低毅力等特质与学习结果之间的关系。他们将调查这些属性如何导致负面行为，如玩弄系统而不是学习材料、非任务行为、随意询问和帮助回避。这项研究将使用学生属性的调查方法、使用教育软件过程中学生行为的自动检测器以及学生学习的方法来开发不同的学生行为如何导致学习结果改善的模型。这些模型被用来检验哪些行为在学生属性与学习之间的关系中起到了中介作用，以及这些行为如何起到了中介作用。研究人员将使用路径分析等统计模型来检验他们的假设。这项研究将在500名不同种族和经济状况的中学生中进行，他们是这些学校科学评估项目的一部分。这些发现旨在作为创建新的教育软件和课堂实践的基础，这些软件和课堂实践在实时识别低毅力、低自我效能感和非生产性目标定向的学生。通过对不同人群的研究，在一个许多美国学生都落后的领域，该项目有可能让目前被剥夺了科学学习权利的学习者受益。