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

该奖项是根据2009年《美国复苏和再投资法案》(Public Law 111-5)资助的。利用数据创建智能学习技术具有独特的潜力，可以改变美国的教育体系，建立一种低成本的方法，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教师创造技术，使个性化教学能够快速创建，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大受众，包括传统上在STEM领域代表性不足的学生，都能获得个性化学习支持。该项目将(1)开发计算方法，以从数据中得出认知模型，这些数据可用于通过指导、反馈和帮助来支持个体学习者；(2)开发提供学生支持的方法，该方法利用数据来提供基于诸如学生反应频率、未来出错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，供教师实时从学生数据中学习，以使教师和教学设计师能够针对实际的、而不是感知到的学生问题领域而定制教学；以及(4)对教学效果进行正式的经验性评估。新软件将使用数据驱动的方法，为逻辑、离散数学和其他STEM领域的教和学构建适应性支持。从计算机辅助学习环境中广泛但易处理的学生表现数据中，将自动构建学生认知模型。这些认知模型将建立在研究人员之前的工作基础上，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S的学习路径，并提供情景提示。机器学习技术将被用于将特定于问题的模型扩展为更一般的认知模型，以引导新教师的建设并了解学生的学习情况。对于教师和学习研究人员，将开发基于网络的可视化和分析工具，以图形和交互方式模拟学生的解决方案，并使用性能数据进行注释，这些数据反映了出现频率、未来出错的趋势以及与最终解决方案的接近程度。通过这些新的教师和工具，将进行实验，以调查学生在各种环境和领域的学习，包括逻辑、代数和化学。一个由不同学生和同事组成的团队将为这项研究带来跨学科的专业知识，并广泛分享研究结果。

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.