HCC: Collaborative Research: Affective Learning Companions: Modeling and supporting emotion during learning

情绪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更好。然而，情感和情感在课堂实践中往往被忽视或边缘化。这个项目将帮助纠正情感与认知的失衡。研究人员将开发情感学习伙伴，这是一种实时计算代理，可以推断情感，并利用这些知识来提高学生的表现。目标是确定学生在任何时间点的情感状态，并提供适当的支持，以改善学生的长期学习。情绪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。在一个由四个传感器(皮肤电导手套、压力鼠标、人脸识别摄像头和姿势传感设备)组成的研究平台内，针对五个独立的情感变量(挫折、动机、自信、无聊和疲劳)进行研究。情绪反馈方法包括使用各种干预措施(鼓励评论、过去表现的图表)，根据类型(解释、提示、样例)和时机(紧随答案之后，经过一段时间)而有所不同。将对哪些干预措施进行评估，以确定哪些措施能最有效地提高绩效，哪些情况下的干预措施效果最佳。机器学习优化算法寻找进一步吸引处于不同情感和认知状态的个别学生的策略。动画代理通过适当的手势和与学生成绩水平和任务复杂性相关的同理心反馈来增强。马萨诸塞州和亚利桑那州约500名来自不同种族和经济背景的学生将参加这项研究。这项研究的更广泛影响是，它有可能开发基于计算机的辅导教师，更好地解决学生多样性问题，包括代表性不足的少数族裔和残疾学生。这里提出的解决方案提供了科学内容的不同表现形式、通过材料的不同途径和不同的互动方式；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绪的理解，将其作为个体学习差异的预测指标，揭示情绪、认知能力和性别对不同学习形式的影响程度。

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.