DIP: Collaborative Research: Impact of Adaptive Interventions on Student Affect, Performance, and Learning

影响学习的一个主要因素是学生的情绪和他们的总体情感状态。鉴于情感在学习活动中扮演的关键角色，人们对开发感知情感的技术非常感兴趣也就不足为奇了。然而，这项工作的绝大多数集中在对情感进行建模，即设计能够推断学生在与智能教学系统(ITS)交互时的感觉的计算模型。虽然情感建模是为学生的情感需求提供适应性支持的关键第一步，但在系统地探索情感干预对学生表现、学习、情感和态度的影响方面，即如何应对学生出现的挫折、焦虑、无聊和绝望等情绪，很少有工作可做。这项研究通过分析为个别学生和学生群体量身定做不同类型的干预措施的价值来填补这一空白。该项目有两个主要目标。首先，它阐述了如何通过各种干预措施应对基于计算机的学习环境中的负面学生情绪(例如，挫折、焦虑、无聊)。其中一些对应于专门设计的数字角色，整合到学习环境中，旨在充当学生的学习伙伴。这些代理通过(A)非语言行为(例如，让角色在应对学生挫折时表现出同理心)、(B)针对学生的认知和元认知技能以及动机和情感的信息来支持学生。其他干预措施包括支持学生之间的合作，以在被发现时缓解负面情绪状态。这些干预措施的影响是通过对800名学生进行的一系列8个实验来调查的。这些实验有助于揭示解决学生情感问题的一般规定原则。这项研究实现的第二个目标是，这些实验为继续扩展和验证现有的情绪模型提供了有价值的数据。具体地说，该项目三角测量并整合了学习中部分重叠的模型和情感结构的复杂空间(即情感、态度、到来的情绪、动机、专心使用或误用软件)。该项目完善了几个成熟的模型，特别是情绪的控制值理论，为教育软件中的情绪领域提供了一个更稳定的理论框架。这项研究是独一无二的，也是开创性的，因为很少有研究人员针对学生在课堂上的情绪，收集关于学习过程中情绪的细粒度数据，或者评估特定情感治疗的影响。使用辅导系统的学生已经显示出统计上的显著收获和学习结果，以及更多的积极情绪和态度。新的情感干预将极大地增加这些系统的广泛影响。本研究正在发展：(1)关于如何应对学生情感的规定性原则；(2)关于认知、情感和元认知干预对情绪和学习的影响的新理解；(3)对学习中个体差异的新理解，揭示情绪、认知能力和性别对学习的影响程度；(4)对个体差异敏感的教学；这项研究还包括：(1)增加经常回避STEM职业的代表不足的人群(妇女和少数群体)对数学的参与；(2)创造广泛的基于网络的技术，通过满足学生的情感和社会需求来帮助更多的学生参与进来；以及(3)解决一刀切的教育方法，通过对内容和材料呈现方式的替代来回应学生的个体需求。

A major factor influencing learning is students' emotions and their general affective state. Given the pivotal role that affect plays in learning activities it is not surprising that there has been a good deal of interest in developing affect-aware technologies. The overwhelming majority of this work, however, has focused on modeling affect, i.e., designing computational models capable of inferring how students are feeling while interacting with an Intelligent Tutoring System (ITS). While modeling of affect is a critical first step in providing adaptive support tailored to students' affective needs, very little work exists on systematically exploring the impact of affective interventions on students' performance, learning, affect and attitudes, i.e., how to respond to students' emotions such as frustration, anxiety, boredom, and hopelessness as they arise. The research fills this gap by analyzing the value of tailoring different types of interventions to negative affective states for individual students and groups of students. The project has two main goals. First, it addresses how to respond to negative student emotion (e.g., frustration, anxiety, boredom) in computer-based learning environments through a variety of interventions. Some correspond to specially-designed digital characters, integrated into the learning environment, which are intended to act like students' learning companions. These agents support students through (a) non-verbal behaviors (e.g., having the characters express empathy in response to student frustration), (b) messages targeting students' cognitive and meta-cognitive skills, as well as motivation and affect. Other interventions involve supporting collaboration between students to mitigate negative emotional states when detected. The impact of these interventions are investigated through a series of eight experiments with a total of 800 students. These experiments help to unveil general prescriptive principles to address student affect. The second goal accomplished by this research is that the experiments provide valuable data to continue to extend and validate existing models of emotion. Specifically, the project triangulates and integrates a complex space of partially overlapping models and constructs of affect in learning (i.e. emotions, attitudes, incoming moods, motivation, engaged use or misuse of software). The project refines several well-established models, in particular the control-value theory of emotions to provide a more stable theoretical framework for the field of emotions in educational software. This research is unique and ground breaking, as few researchers have targeted students' emotion in classrooms, gathered fine grained data on emotions during learning, or assessed the impact of specific affective treatments on a moment-to-moment basis. Students using the tutoring systems have already shown statistically significant gains and learning outcomes, as well as increased positive affect and attitudes. The new affective interventions will greatly increase the broad impact of these systems.This research is developing: (1) prescriptive principles about how to respond to student affect; (2) new understanding about the impact of cognitive, affective, and meta-cognitive interventions on emotions and learning; (3) new understanding about individual differences in learning, unveiling the extent to which emotion, cognitive abilities, and gender impact learning; (4) instruction that is sensitive to individual differences; and (5) refined theories of student emotion.This research is also: (1) increasing participation in mathematics of underrepresented populations (women and minorities) who often avoid STEM careers; (2) creating broad access to web-based technologies that help to engage more students by addressing their affective and social needs; and (3) addressing the one-size-fits-all approach to education, by responding to individual student needs with alternative representations of content and pathways through which material is presented.