HCC: Small: Modeling and Supporting Creativity During Collaborative STEM Activities

HCC：小：在协作 STEM 活动中建模和支持创造力

• 批准号: 1319645
• 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: 2013-08-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319645&HistoricalAwards=false
• 关键词: HCC; Small; Modeling; Supporting; Creativity

This research will advance a novel technological approach that relies on machine learning techniques in general and Natural Language Processing (NLP) in particular to develop models and support for creativity during collaborative science, technology, engineering, and mathematics (STEM) educational activities. We will extend existing educational software with NLP capabilities to automatically assess and subsequently support creativity during collaborative tasks. The research questions are: (1) Which factors influence moment-by-moment creativity during collaborative problem solving activities? (2) How can NLP be used to build student models that detect those factors? (3) How can an ITS use this information to create personalized interventions to support creativity? The first phase in this research will collect data from students solving problems in pairs with an educational application to identify factors that are relevant to creativity processes and outcomes. These data will be used to derive computational student models for automatically assessing student creativity in terms of both moment-to-moment processes and outcomes through machine learning methodologies focusing on an NLP approach. In addition to providing automatic assessment, the models will also inform factors that influence creativity during collaboration through educational data mining techniques. The final phase of the work will design and test a set of interventions to foster creativity during collaborative activities. Using data corresponding to pairs of students solving open-ended STEM-based problems, this research will develop a rich and nuanced understanding of creativity processes and outcomes in collaborative contexts, and how these relate to knowledge, affect and creative thinking styles. Relying on that understanding, it will develop and evaluate novel student models that recognize salient, creativity-related events through NLP techniques, as well as personalized support for creativity during collaborative activities and evaluating that support through an experiment with university students. This project will pave the way for a new class of collaborative cyberlearning technologies to both assess and foster creativity, through just-in-time personalized support based on easily deployed NLP-based student models.

这项研究将推进一种新的技术方法，该方法依赖于一般的机器学习技术和自然语言处理（NLP），特别是在协作科学、技术、工程和数学（STEM）教育活动中开发模型和支持创造力。我们将扩展现有的具有NLP功能的教育软件，以自动评估并随后在协作任务中支持创造力。研究问题为：(1)在协作解决问题活动中，哪些因素会影响每时每刻的创造力？(2)如何使用NLP来构建检测这些因素的学生模型？(3)智能交通系统如何利用这些信息创建个性化的干预措施来支持创造力？本研究的第一阶段将收集与教育应用程序成对解决问题的学生的数据，以确定与创造力过程和结果相关的因素。这些数据将用于推导计算学生模型，通过专注于NLP方法的机器学习方法，自动评估学生在实时过程和结果方面的创造力。除了提供自动评估之外，这些模型还将通过教育数据挖掘技术告知影响协作过程中创造力的因素。工作的最后阶段将设计和测试一套干预措施，以在合作活动中培养创造力。通过使用学生解决开放式stem问题的成对数据，本研究将对协作环境下的创造力过程和结果，以及这些与知识、影响和创造性思维方式的关系，形成丰富而细致的理解。基于这种理解，它将开发和评估新的学生模型，通过NLP技术识别突出的、与创造力相关的事件，以及在合作活动中对创造力的个性化支持，并通过大学生实验来评估这种支持。该项目将为新型协作网络学习技术铺平道路，通过基于易于部署的基于nlp的学生模型的即时个性化支持，评估和培养创造力。