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学、机器学习和认知科学的最新进展。计算拓扑学在学习理论中的应用是新的和创新的。预计这些新工具将为学生学习微积分提供关键的见解。由NSF的教育和学习研究(REAL)计划资助，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学生通过大规模开放在线课程(MOOC)系统(和其他数据源)学习微积分所产生的数据形状。这个项目将连接来自三个领先学习平台的数据分析，以更好地了解学生对微积分的学习。一个潜在的高回报是部署更丰富的测试和表现数据分析，以支持推荐系统，以支持学生实现他们的学习目标。此外，该项目将在通常不相互作用的学科之间建立一个新的研究人员社区。拟议的工作路线有可能从根本上改变我们对评估数据的思考方式，以支持数学学习，也许还有其他STEM内容。

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.