CAREER: Educational Data Mining for Student Support in Interactive Learning Environments

职业：在交互式学习环境中为学生提供教育数据挖掘支持

• 批准号: 0845997
• 负责人: Martin Ribarsky
• 金额: $ 64.7万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845997&HistoricalAwards=false
• 关键词: CAREER; Educational; Data; Mining; Student

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Creating intelligent learning technologies from data has unique potential to transform the American educational system, by building a low cost way to adapt learning environments to individual students, while informing research on human learning. This project will create the technology for a new generation of data-driven intelligent tutors, enabling the rapid creation of individualized instruction to support learning in science, technology, engineering, and mathematics (STEM) fields. This has the potential to make individualized learning support accessible for a broad audience, from children to adults, including students that are traditionally underrepresented in STEM fields. This project will (1) develop computational methods to derive cognitive models from data that can be used to support individual learners through guidance, feedback, and help; (2) develop approaches to providing student support that leverage data to provide hints and guidance based on information such as frequency of student responses, probability of future errors, and solution efficiency; (3) develop interactive visualization tools for teachers to learn from student data in real time, to allow teachers and instructional designers to tailor instruction to address actual, rather than perceived, student problem areas; and (4) conduct formal empirical evaluations of pedagogical effectiveness. The new software will construct adaptive support for teaching and learning in logic, discrete mathematics, and other STEM domains using a data-driven approach. From the extensive but tractable student performance data in computer-aided learning environments, student cognitive models will be automatically constructed. These cognitive models will build on the investigator's prior work using Markov Decision Processes and dimensionality reduction methods that leverage past data to assess student performance, direct a student?s learning path, and provide contextualized hints. Machine learning techniques will be used to expand problem-specific models into more general cognitive models to bootstrap the construction of new tutors and learn about student learning. For teachers and learning researchers, web-based visualization and analysis tool will be developed to graphically and interactively model student solutions annotated with performance data that reflect frequency, tendency to commit future errors, and closeness to a final solution. Through these new tutors and tools, experiments will be conducted to investigate student learning in a variety of contexts and domains, including logic, algebra, and chemistry. A team of diverse students and colleagues will be engaged to bring interdisciplinary expertise to this research and share findings broadly.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。从数据中创建智能学习技术具有独特的潜力，可以通过建立一种低成本的方式来改变美国教育系统，使学习环境适应个别学生，同时为人类学习研究提供信息。该项目将为新一代数据驱动的智能导师创造技术，从而快速创建个性化教学，以支持科学，技术，工程和数学（STEM）领域的学习。这有可能使从儿童到成人的广泛受众获得个性化的学习支持，包括传统上在STEM领域代表性不足的学生。该项目将（1）开发计算方法，从数据中导出认知模型，通过指导，反馈和帮助来支持个人学习者;（2）开发提供学生支持的方法，利用数据提供基于学生响应频率，未来错误概率和解决方案效率等信息的提示和指导;（3）开发交互式可视化工具，使教师能够从真实的学生数据中学习，使教师和教学设计者能够针对实际而不是感知的学生问题领域调整教学;（4）对教学效果进行正式的经验评估。 新软件将使用数据驱动的方法为逻辑，离散数学和其他STEM领域的教学和学习构建自适应支持。从广泛的，但易于处理的学生在计算机辅助学习环境中的表现数据，学生的认知模型将被自动构建。这些认知模型将建立在调查员的先前工作，使用马尔可夫决策过程和降维方法，利用过去的数据来评估学生的表现，指导学生？的学习路径，并提供情境化的提示。机器学习技术将用于将特定问题模型扩展为更通用的认知模型，以引导新导师的构建并了解学生的学习情况。为教师和学习研究人员，基于网络的可视化和分析工具将开发图形和交互式模型的学生解决方案与性能数据，反映频率，倾向于犯未来的错误，并接近最终的解决方案注释。通过这些新的导师和工具，将进行实验，以调查学生在各种背景和领域，包括逻辑，代数和化学的学习。一个由不同的学生和同事组成的团队将致力于为这项研究带来跨学科的专业知识，并广泛分享研究结果。