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(如数学)相关的认知专长的关键，而且也是提高他们的情感(如对该领域的兴趣)和元认知技能(有效学习所需的领域独立能力)的关键。鉴于计算机在今天的教室中变得无处不在，因此有机会开发教育技术，通过适应给定学生的独特需求的个性化教学来最大限度地提高学生的学习能力。实现这种适应的先决条件是技术能够识别学生的兴趣状态(例如，学生对目标领域的了解，他们在学习过程中的感受，以及他们的学习策略)。此任务由用户模型组件完成。传统上，用户模型关注的是学生的认知状态。然而，所有这三个因素(认知、情感、元认知、CAM)都会影响学习结果，因此在建模过程中需要考虑到这三个因素。这样做的一个关键困难与这样一个事实有关，即模型几乎没有关于较高水平状态的直接信息，例如，与情感或元认知相关的信息。 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来构建CAM状态的模型：(1)学生与目标技术的交互和(2)传感设备(眼球跟踪，EEG)。这些模型将针对四个在HQP培训、研究贡献和实际影响方面最有潜力产生影响的教育背景进行设计，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授机器人代理中学习。结果将对应于能够识别与相应上下文最相关的CAM状态的每个这些上下文的用户模型，以及用于设计和评估来自交互和传感器数据的模型的一般技术。

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.