Beyond Boredom: Modeling and Promoting Engagement during Complex Learning

该提案的核心研究问题是学习者之间的互动（即，个体差异），指导材料（即，文本），和学习活动（即，任务）调节批判性思维技能和科学推理学习期间的参与度。拟议的研究将通过以下方式实现这一目标：（a）系统地调查促进或阻碍参与的机制，以及（B）利用这些见解制定干预措施，促进深度学习期间持续和富有成效的参与轨迹。 这项工作将在孟菲斯大学进行。 研究对象为本科生。研究设计包括四个实验，其中学习收益和自我报告的参与，生理唤醒，眼睛注视模式，面部特征将被跟踪，而学习者学习教学文本。理解这些文本的掌握需要积极参与，因为学习者产生推论，理解因果关系，识别问题，区分实验设计的质量，并提出诊断性问题。将使用非线性时间序列分析技术进行分析，例如复发定量分析。项目评价将包括一个专家咨询委员会，该委员会将用于严格审查调查人员的调查结果和解释。 此外，研究人员将开发和验证一个基于网络的计算机程序，该程序可以根据个人学习者的需求和学习风格动态调整教学文本和学习活动，以提高参与度。该研究将通过系统的实验来平衡理论构建和模型测试的理论目标，以及开发创新的高级学习技术的实际目标，这些技术旨在促进参与和学习困难的主题。这项研究对于STEM教育领域正在进行的努力非常重要，以提高STEM主题学习的参与度和生产力。 如果研究成功，衍生的知识和工具将是重大的贡献，并可以广泛应用于其他智能教学系统，其他教学技术，并在我们对学生学习的理解一般。 这项研究在两个方面具有潜在的变革性。 首先，它将在参与的真实的时间内提供对学生、任务和材料的微观、关系层面的详细理解。其次，基于这些发现创建智能辅导系统，可以随时“纠正”低水平的学生参与度，从而提高学习效率，同时提高学生的满意度和参与度。 传播将包括向公众提供技术工具以及对学术文献的贡献。

The core research question of this proposal is how interactions between learners (i.e., individual differences), instructional materials (i.e., the text), and learning activities (i.e., the task) modulate engagement during learning of critical thinking skills and scientific reasoning. The proposed research will address this goal by: (a) systematically investigating the mechanisms that facilitate or hinder engagement, and (b) leveraging these insights towards the development of interventions that promote persistent and productive engagement trajectories during deep learning. The work will be conducted at the University of Memphis. The research subjects will be undergraduate students. The research design includes four experiments in which learning gains and self-reported engagement, physiological arousal, eye gaze patterns, and facial features will be tracked while learners study instructional texts. Comprehending these texts for mastery requires active engagement as learners generate inferences, understand causality, identify problems, discriminate the quality of experimental designs, and ask diagnostic questions. Analyses will be conducted using nonlinear time series analysis techniques, such as recurrence quantification analysis. The project evaluation will include an expert advisory committee that will be used to critically review the investigators' findings and interpretations. In addition, the investigators will develop and validate a web-based computer program that dynamically tailors both the instructional text and the learning activity to the needs and learning styles of individual learners to enhance engagement. The proposed research will balance the theoretical goal of theory building and model testing via systematic experimentation with the practical goal of developing innovative advanced learning technologies that aspire to promote engagement and learning of difficult subject matter.This research is important in the STEM education field's ongoing efforts to increase engagement and the productivity of learning of STEM subject matter. If the research is successful, the derivative knowledge and tools will be significant contributions and could be applied widely in other intelligent tutoring systems, other instructional technologies, and in our understanding of student learning in general. This research is potentially transformative in two ways. First, it will provide a detailed understanding in real time of engagement at the micro, relational level of student, task, and materials. Second, the creation of an intelligent tutoring system based on these findings holds the possibility of being able to 'correct' low levels of student engagement on a moment-to-moment basis and therefore boost learning productivity while increasing student satisfaction and engagement with the experience. Dissemination will include the public availability of the technological tools as well as contributions to the scholarly literature.