Shape of Educational Data

这项关于STEM教育评估数据“形状”的研究将利用计算拓扑、机器学习和认知科学的最新进展。计算拓扑在学习理论中的应用是一种新的创新。预计这些新工具将为学生学习微积分提供重要的见解。由美国国家科学基金会的教育与学习研究（REAL）项目资助，这项探索性研究的早期概念拨款将应用先进的拓扑、几何和贝叶斯方法来分析通过大规模开放在线课程（MOOC）系统（和其他数据源）学习微积分的学生生成的数据的形状。该项目将结合三个主要学习平台的数据分析，以更好地了解学生的微积分学习情况。一个潜在的高回报是部署更丰富的测试和性能数据分析，以支持推荐系统，以支持学生实现他们的学习目标。此外，该项目将培育一个新的跨学科研究人员社区，这些研究人员通常不会相互作用。拟议的工作有可能从根本上改变我们对支持数学学习和其他STEM内容的评估数据的看法。

This study of the 'shape' of STEM educational assessment data will exploit recent advances in computational topology, machine learning and cognitive science. Applications of computational topology to learning theory are both new and innovative. It is anticipated that these new tools will provide critical insights into student learning of Calculus.Funded by NSF's Research on Education and Learning (REAL) program, this early concept grant for exploratory research will apply advanced topological, geometric, and Bayesian methods to analyze the shape of data generated by students taking Calculus via a massive open online course (MOOC) system (and other data sources). This project will link data analyses from three leading learning platforms in the service of better understanding student learning of Calculus. One potential high payoff is the deployment of richer analyses of test and performance data to support a recommendation system to support to students reaching their learning goals. Further, the project will seed a new community of researchers across disciplines that typically do not interact. The proposed line of work has the potential to fundamentally change the way we think about assessment data in support of mathematics learning and perhaps other STEM content.