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

DIP：协作研究：适应性干预对学生情感、表现和学习的影响

• 批准号: 1324880
• 负责人: Winslow Burleson
• 金额: $ 50万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1324880&HistoricalAwards=false
• 关键词: DIP; Collaborative; Research; Impact; Adaptive

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.

影响学习的一个主要因素是学生的情绪和他们的一般情感状态。考虑到情感在学习活动中的关键作用，人们对开发情感感知技术产生了很大的兴趣也就不足为奇了。然而，这项工作的绝大多数都集中在模拟影响上，即，设计能够推断学生在与智能辅导系统（ITS）交互时的感受的计算模型。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，但很少有工作系统地探索情感干预对学生表现，学习，情感和态度的影响，即，如何回应学生的情绪，如挫折，焦虑，无聊和绝望，因为他们出现。这项研究填补了这一空白，通过分析量身定制不同类型的干预措施，以个别学生和学生群体的负面情绪状态的价值。 该项目有两个主要目标。首先，它解决了如何应对学生的负面情绪（例如，挫折，焦虑，无聊）在计算机为基础的学习环境中通过各种干预。有些对应于专门设计的数字字符，集成到学习环境中，旨在充当学生的学习伙伴。 这些代理人通过（a）非语言行为（例如，使角色对学生的挫折感做出反应而表达同情），（B）针对学生的认知和元认知技能以及动机和情感的信息。其他干预措施包括支持学生之间的合作，以减轻发现时的负面情绪状态。这些干预措施的影响进行了调查，通过一系列的8个实验，共800名学生。这些实验有助于揭示解决学生情感的一般规定性原则。本研究完成的第二个目标是，实验提供了有价值的数据，以继续扩展和验证现有的情感模型。具体来说，该项目三角测量和整合了一个复杂的空间，部分重叠的模型和结构的影响学习（即情绪，态度，传入的情绪，动机，从事使用或滥用软件）。该项目提炼了几个成熟的模型，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。这项研究是独特且开创性的，因为很少有研究人员针对学生在课堂上的情绪，收集有关学习期间情绪的细粒度数据，或随时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显著的收益和学习成果，以及积极的影响和态度的增加。新的情感干预将大大增加这些系统的广泛影响。这项研究正在制定：（1）关于如何应对学生情感的规定性原则;（2）关于认知、情感和元认知干预的影响的新认识对情绪和学习;（3）对学习中个体差异的新认识，揭示了情绪、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;（5）完善的学生情感理论。本研究还：（1）增加代表性不足人群的数学参与（女性和少数民族）经常避免STEM职业;（2）创造广泛的网络技术，通过满足学生的情感和社会需求，帮助更多的学生参与进来;以及（3）解决“一刀切”的教育方法，以不同的内容表示和材料呈现途径来满足个别学生的需求。