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更好。然而，在课堂实践中，情感和情感往往被忽视或边缘化。这个项目将有助于纠正情绪与认知的不平衡。研究人员将开发情感学习伙伴，这是一种实时计算代理，可以推断情绪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，以长期改善学生的学习。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。在一个由皮肤电导手套、压力鼠标、人脸识别摄像头和姿态传感设备组成的四个传感器组成的研究平台中，针对五个独立的情感变量（挫折、动机、自信、无聊和疲劳）。情绪反馈方法包括使用各种干预措施（鼓励性评论，过去表现的图表），根据类型（解释，提示，工作示例）和时间（紧随答案之后，经过一段时间）进行调整。将对干预措施进行评估，以确定哪种干预措施最能提高绩效，并在哪种情况下实施。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别学生的政策。动画代理通过适当的手势和移情反馈来增强与学生成绩水平和任务复杂性相关的能力。大约500名来自马萨诸塞州和亚利桑那州的不同种族和经济背景的学生将参加。这项研究更广泛的影响在于，它有可能开发出基于计算机的导师，更好地解决学生的多样性问题，包括代表性不足的少数民族和残疾学生。这里提出的解决方案提供了科学内容的替代表示，通过材料的替代路径和替代交互方式；因此，可能导致高度个性化的科学学习。此外，该项目有可能推进我们对情感作为学习个体差异预测因子的理解，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.