RI: Small: CompCog: Pique: A Cognitive Model of Curiosity for Personalizing Sequences of Learning Resources

RI：小：CompCog：Pique：用于个性化学习资源序列的好奇心认知模型

• 批准号: 1618810
• 负责人: Mary Lou Maher
• 金额: $ 44.94万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-15 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618810&HistoricalAwards=false
• 关键词: RI; Small; CompCog; Pique; Cognitive

One of the most significant challenges in education is to simultaneously provide personalization and scale. How can each learner in an online class of hundreds or thousands be provided with knowledge and challenges that suit them personally? This project will develop an AI system for personalized learning that is inspired by cognitive models of curiosity, creativity and intrinsic motivation. Pique (short for the "Personalized Curiosity Engine") is based on understanding what makes an individual learner curious, and then recommending resources that will stimulate their curiosity. Pique's cognitive model of its learner uses natural language processing techniques to figure out what sequences of resources will be familiar enough to be accessible, but sufficiently new to not be boring. Pique is a novel cognitive system drawing on technologies from intelligent tutoring systems, computational creativity, and natural language processing. Its key contribution is combining a cognitive model of curiosity with educational recommender systems. We will evaluate the effectiveness of Pique first with simulations and then with students at a large comprehensive public university. Evaluation will take the form of a comparison between the full Pique system and a modified version with its cognitive model of curiosity disabled. This will enable us to determine whether recommending resources that are simultaneously curiosity-stimulating and fit to the task is more effective than recommending resources that are just fit to the task. Given the interdisciplinary nature of this research we will disseminate our results broadly, including to the educational technology, cognitive systems, information retrieval, and computational creativity communities.

教育领域最大的挑战之一是同时提供个性化和规模化。如何才能为数百人或数千人的在线课堂上的每一位学习者提供适合他们个人的知识和挑战？该项目将开发一个个性化学习的人工智能系统，该系统的灵感来自好奇心、创造力和内在动机的认知模型。PICK(个性化好奇心引擎的缩写)是基于了解是什么让单个学习者产生好奇心，然后推荐能够激发他们好奇心的资源。皮克对其学习者的认知模型使用自然语言处理技术来计算出哪些资源序列足够熟悉，可以访问，但足够新，不会令人厌烦。PICK是一种新型的认知系统，它借鉴了智能教学系统、计算创造力和自然语言处理等技术。它的主要贡献是将好奇心的认知模型与教育推荐系统相结合。我们将首先通过模拟评估Pique的有效性，然后再与一所大型综合性公立大学的学生进行评估。评估将采取完整的Pique系统与禁用其好奇心认知模型的修改版本之间的比较形式。这将使我们能够确定，推荐既能激发好奇心又适合这项任务的资源是否比推荐恰好适合这项任务的资源更有效。鉴于这项研究的跨学科性质，我们将广泛传播我们的成果，包括教育技术、认知系统、信息检索和计算创造力社区。