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

虚拟学习环境是STEM领域个性化学习日益重要的组成部分。新技术（包括生物识别和神经成像）为不显眼地测量学生的参与和学习提供了新的机会。该项目是与NSF于2014年10月召集的数据密集型研究改进教学的想法实验室合作开发的，将利用这些技术提供内隐学习与外显学习相联系的方法的基础知识，以开发适合不同学习者的教育相关游戏。来自TERC、Landmark学院和麻省理工学院的研究人员将合作研究以下三者之间的关系：(1)数字游戏（“冲动”）中的游戏模式；(2)学生注意力（通过眼睛和头部跟踪设备测量）；(3)学生学习牛顿第一和第二定律。研究人员将收集游戏中嵌入的学生参与度和学习成果的指标。研究对象将包括一组神经系统多样化的学生，包括普通本科生和患有注意缺陷多动障碍和/或自闭症谱系障碍的学生。研究人员将开发一个视觉注意力和游戏模式的模型，检查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到与当前游戏状态相关的物体上。然后，他们将把最初的模型与学生参与的测量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，在不同的残疾学习者中使用自适应版本和正常版本的游戏。

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.