Beyond Boredom: Modeling and Promoting Engagement during Complex Learning

这一建议的核心研究问题是学习者(即个体差异)、教学材料(即课文)和学习活动(即任务)之间的互动如何调节批判性思维技能和科学推理的学习投入。拟议的研究将通过以下方式解决这一目标：(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，以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，在这些实验中，学习者在学习教学文本时将跟踪学习成果和自我报告的参与度、生理唤醒、眼睛凝视模式和面部特征。理解这些文本以掌握需要积极的参与，因为学习者产生推理，理解因果关系，识别问题，辨别实验设计的质量，并提出诊断性问题。分析将使用非线性时间序列分析技术，例如递归量化分析。项目评估将包括一个专家咨询委员会，该委员会将被用来严格审查调查人员的调查结果和解释。此外，调查人员将开发和验证一个基于网络的计算机程序，该程序根据个别学习者的需要和学习方式动态调整教学文本和学习活动，以提高参与度。这项拟议的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和开发创新的高级学习技术的实践目标，这些技术旨在促进对困难学科的参与和学习。这项研究对STEM教育领域不断努力提高STEM主题的参与度和学习效率具有重要意义。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，以及我们对学生学习的总体理解。这项研究在两个方面具有潜在的变革性。首先，它将提供学生、任务和材料的微观、关系级别的实时参与的详细了解。其次，基于这些发现创建的智能辅导系统有可能随时纠正学生的低参与度，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.