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.

本研究将探讨在电脑教学环境下，科学方法学习过程中认知与情绪的互动。 主要的焦点将放在僵局、认知不平衡和情感认知混乱状态之间的关系上。困惑与学习收获相关，因为它是认知不平衡的诊断，这种状态发生在学习者面临目标障碍、矛盾、不协调、异常、冲突和系统故障时。认知平衡通常在思考、反思、解决问题和其他努力的认知活动后恢复。因此，挑战、困惑和有效地混淆学习者的教学策略是教育中典型的信息传递系统的刺激替代品，这些系统在学习者的舒适区促进浅层知识，但很少深入理解。本研究将开发辅导干预，诱导，跟踪和调节混乱和认知不平衡的学习者的头脑，以及认知和情感机制，恢复认知平衡。 该研究有三个具体目标：（1）通过开发实验诱导僵局，认知不平衡和由此产生的混乱的教程干预来促进深度学习;（2）整合检测和跟踪相关混乱的传感设备和信号处理算法;（3）开发对影响敏感的教学策略，以帮助学习者调节他们的混乱。 这三个目标将通过增强现有的智能辅导系统（ARIES，获取研究调查和评估技能）来实现，该系统具有自动评估情感和认知以及智能处理情感的技术。最先进的感测设备根据对话历史、面部表情和身体姿势检测学习期间的相关情绪（困惑、沮丧、无聊、流动/参与、喜悦、惊讶）。 ARIES系统通过展示有缺陷的科学方法的案例研究来促进科学探究技能，并要求学习者对研究的科学价值提出深思熟虑的批评。这些批评鼓励（a）进行推论、构建因果模型、识别问题和提出诊断性问题的一般认知过程，以及（B）直接针对科学推理的技能，例如陈述假设、识别因变量和自变量、隔离设计中的潜在混淆、解释数据趋势以及确定数据是否支持预测。 学生与ARIES通过对话trialogues在自然语言与两个动画代理：导师代理和同行代理。 当代理人产生与学生所知道的矛盾，冲突和冲突的信息时，就会产生认知不平衡。 正确的信息最终会出现在三方对话中，从而恢复认知平衡。 该项目更广泛的意义和重要性是推进科学教育，智能学习环境和人机界面。人们普遍认为，美国学生和成年人的科学理解水平需要提高，与其他几个国家相比并不有利。拟议的研究将通过开发技术干预措施来帮助填补这一空白，以加强公民和有抱负的科学家的批判性思维，复杂推理和解决科学问题所需的技能。 该项目将开发智能学习环境，以进行深度学习，这是技术先进的劳动力所需要的，也是激励学习经验的，这是近几代学生所期望的。 该项目将开发用于检测情感和认知的先进传感设备，这一贡献将影响人机交互，认知科学和学习科学领域。