HCC: Collaborative Research: Affective Learning Companions: Modeling and Supporting Emotion During Learning

情感和动机是学习的基础;内在动机高的学生往往比动机低的学生表现得更好。然而，情感和情绪往往被忽视或边缘化的课堂实践。这个项目将有助于纠正情感与认知的不平衡。研究人员将开发情感学习伴侣，实时计算代理，推断情绪，并利用这些知识来提高学生的表现。其目标是确定学生的情感状态，在任何时间点，并提供适当的支持，以改善学生的长期学习。情感识别方法包括使用硬件传感器和机器学习软件来识别学生的状态。五个独立的情感变量的目标（挫折，动机，自信，无聊和疲劳）内的研究平台，包括四个传感器（皮肤电导手套，压力鼠标，人脸识别摄像头和姿势传感设备）。情感反馈方法包括使用各种干预措施（鼓励性评论，过去表现的图形），根据类型（解释，提示，工作示例）和时间（立即回答，经过一段时间后）而变化。将对干预措施进行评价，以确定哪些措施最能提高业绩，在哪些情况下最能提高业绩。机器学习优化算法搜索进一步吸引处于不同情感和认知状态的个体学生的策略。动画代理增强与适当的手势和移情反馈学生的成绩水平和任务的复杂性。来自马萨诸塞州和亚利桑那州的大约500名种族和经济背景不同的学生将参加这项研究，其更广泛的影响是开发基于计算机的导师，更好地解决学生的多样性，包括代表性不足的少数民族和残疾学生的潜力。这里提出的解决方案提供了科学内容的替代表示，通过材料和交互的替代手段的替代路径;因此，可能导致高度个性化的科学学习。此外，该项目有可能促进我们对情感的理解，将其作为学习中个体差异的预测因素，揭示情感，认知能力和性别对不同形式学习的影响程度。

Emotion and motivation are fundamental to learning; students with high intrinsic motivation often outperform students with low motivation. Yet affect and emotion are often ignored or marginalized with respect to classroom practice. This project will help redress the emotion versus cognition imbalance. The researchers will develop Affective Learning Companions, real-time computational agents that infer emotions and leverage this knowledge to increase student performance. The goal is to determine the affective state of a student, at any point in time, and to provide appropriate support to improve student learning in the long term. Emotion recognition methods include using hardware sensors and machine learning software to identify a student's state. Five independent affective variables are targeted (frustration, motivation, self-confidence, boredom and fatigue) within a research platform consisting of four sensors (skin conductance glove, pressure mouse, face recognition camera and posture sensing devices). Emotion feedback methods include using a variety of interventions (encouraging comments, graphics of past performance) varied according to type (explanation, hints, worked examples) and timing (immediately following an answer, after some elapsed time). The interventions will be evaluated as to which best increase performance and in which contexts. Machine learning optimization algorithms search for policies that further engage individual students who are involved in different affective and cognitive states. Animated agents are enhanced with appropriate gestures and empathetic feedback in relation to student achievement level and task complexity. Approximately 500 ethnically and economically diverse students in Massachusetts and Arizona will participate.The broader impact of this research is its potential for developing computer-based tutors that better address student diversity, including underrepresented minorities and disabled students. The solution proposed here provides alternative representations of scientific content, alternative paths through material and alternative means of interaction; thus, potentially leading to highly individualized science learning. Further, the project has the potential to advance our understanding of emotion as a predictor of individual differences in learning, unveiling the extent to which emotion, cognitive ability and gender impact different forms of learning.