Collaborative Research: Revealing the Invisible: Data-Intensive Research Using Cognitive, Psychological, and Physiological Measures to Optimize STEM Learning

合作研究：揭示无形之物：利用认知、心理和生理措施优化 STEM 学习的数据密集型研究

• 批准号: 1417456
• 负责人: Ibrahim Dahlstrom-Hakki
• 金额: $ 27.04万
• 依托单位: Landmark College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1417456&HistoricalAwards=false
• 关键词: Collaborative; Research; Revealing; Invisible; Data

Virtual learning environments are an increasingly important component of individualized learning in STEM domains. New technologies (including biometry and neuro-imaging) provide new opportunities to unobtrusively measure student engagement and learning. This project-developed in connection with an Ideas Lab on Data-Intensive Research to Improve Teaching and Learning that NSF convened in October 2014 -will utilize these technologies to provide foundational knowledge of the ways in which measures of implicit learning might be linked to explicit learning to develop educationally relevant games that are adaptive to diverse learners. Investigators from TERC, Landmark College, and the Massachusetts Institute of Technology will collaborate to examine the relationships among: (1) patterns of play in a digital game ("Impulse"); (2) student attention (measured from eye- and head-tracking devices); and (3) student learning about Newton's first and second law. The researchers will collect measures of student engagement and learning outcomes embedded in the game. Subjects will comprise a neurodiverse group of students including regular undergraduates and those with Attention Deficit Hyperactivity Disorder and/or Autism Spectrum Disorder. The researchers will develop a model of visual attention and patterns of play, examining the extent to which eye movements are allocated strategically to objects of relevance to the current game state as a student learns in the game. They will then link the initial model with measures of student engagement and conceptual understanding of relevant physical science constructs to refine the model. The refined model will be used to develop a modified game based on the players' attention, and a prototype of the modified game will be tested. The final phase of the research will be a within-subject design with the adaptive version versus the normal version of the game across learners with different profiles of disability.

虚拟学习环境是STEM领域个性化学习的重要组成部分。新技术（包括生物统计学和神经成像）提供了新的机会，以不引人注目地衡量学生的参与和学习。该项目与NSF于2014年10月召开的关于数据密集型研究以改善教学和学习的想法实验室一起开发，将利用这些技术提供有关内隐学习措施可能与外显学习联系起来的方式的基础知识，以开发适应不同学习者的教育相关游戏。 来自TERC、Landmark College和马萨诸塞州理工学院的研究人员将合作研究以下因素之间的关系：（1）数字游戏（“Impulse”）中的游戏模式;（2）学生的注意力（通过眼睛和头部跟踪设备测量）;（3）学生学习牛顿第一定律和第二定律。研究人员将收集嵌入游戏中的学生参与度和学习成果的衡量标准。受试者将包括一组神经多样性的学生，包括普通本科生和那些有注意力缺陷多动障碍和/或自闭症谱系障碍。研究人员将开发一个视觉注意力和游戏模式的模型，研究当学生在游戏中学习时，眼球运动在多大程度上被战略性地分配给与当前游戏状态相关的对象。然后，他们将把初始模型与学生参与度和对相关物理科学结构的概念理解联系起来，以完善模型。 改进后的模型将用于根据玩家的注意力开发修改后的游戏，并测试修改后的游戏原型。研究的最后阶段将是一个主题内的设计与自适应版本与正常版本的游戏在学习者与不同的残疾概况。