Inducing, Tracking, and Regulating Confusion and Cognitive Disequilibrium during Complex Learning

复杂学习过程中诱导、跟踪和调节混乱和认知不平衡

• 批准号: 0834847
• 负责人: Arthur Graesser
• 金额: $ 42万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0834847&HistoricalAwards=false
• 关键词: Inducing; Tracking; Regulating; Confusion; Cognitive

This research will explore interactions between cognition and emotion during the learning of scientific methods in the context of a computer tutoring environment. The primary focus will be on the relations between impasses, cognitive disequilibrium, and the affective-cognitive state of confusion. Confusion correlates with learning gains because it is diagnostic of cognitive disequilibrium, a state that occurs when learners face obstacles to goals, contradictions, incongruities, anomalies, conflicts, and system breakdowns. Cognitive equilibrium is normally restored after thought, reflection, problem solving and other effortful cognitive activities. Therefore, pedagogical tactics that challenge, perplex, and productively confuse learners are stimulating alternatives to the typical information delivery systems in education that promote shallow knowledge in the comfort zone of the learner, but rarely deep comprehension. This research will develop tutorial interventions that induce, track, and regulate confusion and cognitive disequilibrium in the minds of learners, as well as the cognitive and emotional mechanisms that restore cognitive equilibrium. The research has three specific objectives: (1) To promote deep learning by developing tutorial interventions that experimentally induce impasses, cognitive disequilibrium, and the resulting confusion; (2) to integrate sensing devices and signal processing algorithms that detect and track the associated confusion; and (3) to develop affect-sensitive pedagogical strategies to help learners regulate their confusion. The three objectives will be accomplished by augmenting an existing Intelligent Tutoring System (ARIES, Acquiring Research Investigative and Evaluative Skills) with technologies that automate assessment of emotion and cognition, as well as an intelligent handling of emotions. State-of-the-art sensing devices detect relevant emotions during learning (confusion, frustration, boredom, flow/engagement, delight, surprise) on the basis of the dialogue history, facial expressions, and body posture. The ARIES system promotes scientific inquiry skills by presenting case studies that exhibit flawed scientific methods and that require learners to offer thoughtful critiques on the scientific merits of the studies. The critiques encourage (a) the general cognitive processes of drawing inferences, constructing causal models, identifying problems, and asking diagnostic questions and (b) skills that directly target scientific reasoning, such as stating hypotheses, identifying dependent and independent variables, isolating potential confounds in designs, interpreting trends in data, and determining whether data support predictions. Students interact with ARIES through conversational trialogues in natural language with two animated agents: a tutor agent and a peer agent. Cognitive disequilibrium is created when the agents produce messages with contradictions, conflicts, and clashes with what the student knows. Correct information eventually emerges in the trialogue, which restores cognitive equilibrium. The broader significance and importance of the project is to advance science education, intelligent learning environments, and human-computer interfaces. It is widely acknowledged that the level of science understanding among students and adults in the United States needs improvement and does not compare favorably with several other nations. The proposed research will help fill this gap by developing technological interventions to fortify citizens and aspiring scientists with the skills needed for critical thinking, complex reasoning, and problem solving in science. The project will develop intelligent learning environments targeted for deeper learning, which is needed for a technologically sophisticated workforce, and for a motivating learning experience, which is expected in recent generations of students. The project will develop advanced sensing devices for detecting emotions and cognition, a contribution that should impact the fields of human-computer interaction, cognitive science, and the learning sciences.

这项研究将在计算机补习环境中学习科学方法期间的认知与情感之间的互动。 主要重点将放在障碍，认知不平衡和情感认知状态之间的关系上。混乱与学习收益相关，因为它是认知不平衡的诊断​​，这种状态是在学习者面临目标，矛盾，不一致，异常，冲突和系统崩溃的情况下发生的情况。经过思考，反思，解决问题和其他精力认知活动后，通常会恢复认知平衡。因此，挑战，困惑和富有成效的学习者的教学策略正在刺激教育中典型的信息传递系统的替代方案，这些信息传递系统在学习者的舒适区中促进了肤浅的知识，但很少有深刻的理解。这项研究将开发教程干预措施，以诱导，跟踪和调节学习者心目中的混乱和认知不平衡，以及恢复认知平衡的认知和情感机制。 这项研究具有三个特定的目标：（1）通过开发实验引起际交往，认知不平衡和导致的混乱的教程干预措施来促进深度学习； （2）集成检测和跟踪相关混淆的传感设备和信号处理算法； （3）制定对情感敏感的教学策略，以帮助学习者调节他们的困惑。 这三个目标将通过扩大现有的智能辅导系统（白羊座，获得研究调查和评估技能）来实现，这些技术可以使情绪和认知的评估自动化，以及对情感的智能处理。最先进的传感设备根据对话历史，面部表情和身体姿势在学习过程中检测到相关的情绪（混乱，沮丧，无聊，流动/互动，喜悦，惊喜，惊喜）。 白羊座系统通过介绍表现出有缺陷的科学方法的案例研究来促进科学探究技巧，并要求学习者对研究的科学优点提出周到的批评。批评鼓励（a）绘制推断，构建因果模型，识别问题并提出诊断问题和（b）直接针对科学推理的技能的一般认知过程，例如陈述假设，识别依赖性和自变量，识别和自变量，隔离了设计中的潜在混淆，在数据支持中解释了数据支持的趋势，并确定了数据支持是否预测。 学生通过自然语言的对话试验与两种动画代理人：导师代理商和同伴代理商进行互动。 当代理商发出与学生所知道的有关矛盾，冲突和冲突的信息时，就会产生认知不平衡。 正确的信息最终出现在恢复认知平衡的试验中。 该项目的更广泛的意义和重要性是提高科学教育，智能学习环境和人力计算机界面。人们普遍承认，美国学生和成年人之间的科学了解水平需要改进，并且与其他几个国家相比并不相比。拟议的研究将通过制定技术干预措施来填补这一差距，以增强公民和有抱负的科学家的批判性思维，复杂的推理和科学解决问题所需的技能。 该项目将开发针对更深层次学习的智能学习环境，这是技术成熟的劳动力以及激励学习经验所需的，这是最近几代学生所期望的。 该项目将开发高级感应设备，以检测情绪和认知，这应该影响人类计算机互动，认知科学和学习科学领域的贡献。