Extending educational technologies with user models of cognitive, affective and meta-cognitive student states

个性化教学不仅是提高学生与目标领域相关的认知专业知识的关键（例如，数学）而且它们的影响（例如，对领域的兴趣）和元认知技能（有效学习所需的领域独立能力）。鉴于计算机在当今的教室中变得无处不在，因此有机会开发教育技术，通过适应特定学生独特需求的个性化教学来最大限度地提高学生的学习。实现这种适应的先决条件是技术可以识别学生感兴趣的状态（例如，学生对目标领域的了解、学习过程中的感受以及学习策略）。此任务由用户模型组件完成。传统上，用户模型关注学生的认知状态。然而，所有三个要素（认知，情感，元认知，CAM）影响学习成果，因此需要在建模过程中考虑。这样做的一个关键困难在于，模型几乎没有任何关于与情感或元认知相关的更高层次状态的直接信息。 我的研究计划的长期目标是在各种教育背景和应用程序中设计和评估CAM状态的用户模型。在短期内，我的研究议程需要应用机器学习和以专家为中心的方法，基于来自两个来源的数据构建CAM状态的模型：（1）学生与目标技术的交互和（2）传感设备（眼动跟踪，EEG）。这些模型将被设计用于四种教育背景，这些教育背景在HQP培训，研究贡献和实际影响方面具有最大的潜力，包括类比问题解决，协作活动，开放式环境中的创造力以及从教学机器人代理中学习。这些结果将对应于每个上下文的用户模型，这些上下文能够识别与相应上下文最相关的CAM状态，以及用于从交互和传感器数据设计和评估模型的一般技术。

Personalized instruction is key for improving not only students' cognitive expertise related to the target domain (e.g., mathematics) but also their affect (e.g., interest about the domain) and meta-cognitive skills (domain independent abilities needed to learn effectively). Given that computers are becoming ubiquitous in today's classrooms, there is an opportunity to develop educational technologies that maximize student learning through personalized instruction adapted to a given student's unique needs. The prerequisite for realizing this adaptation is that technologies can recognize student states of interest (e.g., what students know about the target domain, how they feel during the learning process, and their learning strategies). This task is accomplished by the user model component. Traditionally, user models have focused on students' cognitive states. However, all three elements (Cognitive, Affective, Meta-cognitive, CAM) impact learning outcomes and so need to be taken into account during the modeling process. A key difficulty in doing so pertains to the fact that there is little if any direct information available to the model on higher level states associated with, for instance, affect or meta-cognition. The long term goal of my research program is to design and evaluate user models for CAM states in a variety of educational contexts and applications. In the short term, my research agenda entails applying machine learning and expert centric approaches to construct models for CAM states based on data coming from two sources: (1) students' interaction with the target technology and (2) sensing devices (eye tracking, EEG). The models will be designed for four educational contexts that have the greatest potential to make an impact in terms of HQP training, research contributions, and practical implications, including analogical problem solving, collaborative activities, creativity in open-ended environments, and learning from teaching robotic agents. The outcomes will correspond to user models for each of these contexts that are capable of recognizing CAM states most relevant to the corresponding context, as well as general techniques for devising and evaluating models from interaction and sensor data.