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Modeling COVID-19 Disruptions via Network Mapping of the Common Core Mathematics Standards

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Computers in Education 6: Best of CoED

Tagged Division

Computers in Education

Page Count

13

Permanent URL

https://peer.asee.org/37513

Download Count

44

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Paper Authors

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Luwen Huang Massachusetts Institute of Technology

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Luwen Huang is a product and visualization specialist. She works on leading product design, developing engineering cycles and achieving product-market fit in early-stage products. Her specialization lies in computer vision, graphics, interaction design and data visualization.

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Kayla M. Bicol

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Karen E. Willcox University of Texas at Austin

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Karen E. Willcox is Director of the Oden Institute for Computational Engineering and Sciences, a Professor of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin, and External Professor at the Santa Fe Institute. Before joining the Oden Institute in 2018, she spent 17 years as a professor at the Massachusetts Institute of Technology, where she served as the founding Co-Director of the MIT Center for Computational Engineering and the Associate Head of the MIT Department of Aeronautics and Astronautics. Prior to joining the MIT faculty, she worked at Boeing Phantom Works with the Blended-Wing-Body aircraft design group. She is a Fellow of the Society for Industrial and Applied Mathematics (SIAM) and Fellow of the American Institute of Aeronautics and Astronautics (AIAA).

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Abstract

This paper presents an educational mapping of the Common Core Mathematics Standards and demonstrates the use of this map to predict disruptions due to the global COVID-19 pandemic. These predictions inform decisions on teaching and learning interventions and curriculum redesign to address deficiencies.

In the spring of 2020, millions of students abruptly shifted to online instruction, and in some cases, no instruction, as COVID-19 disrupted schools nationwide. But this disruption is not simply localized to a single semester: consider, for example, the downstream effects on a fifth grader, who needs to master adding fractions in order to perform more complicated operations in sixth and later grades. The effects of COVID-19 disruptions will be felt in K-12 education for many years to come.

This paper presents a structured approach to modeling and analyzing COVID-19 disruptions to the Common Core Math curriculum. This work builds upon our previously established work of mapping educational data using network models. We construct the Common Core Math curriculum as a network model with nodes representing “Grade Bands”, “Domains” and “Clusters” as given in the Common Core. The network also represents “Micro-outcomes” which are Common Core Standards that have been further divided into more granular statements of learning. For each Micro-outcome node in the network model, we attach additional attributes, such as links to Khan Academy videos that address the specific Micro-outcome. We then draw relationships between the nodes in our network model: between two Micro-outcomes, there may be a has-prerequisite-of relationship that points from one Micro-outcome to the other. This relationship represents the notion that achieving one Micro-outcome is a prerequisite to achieving the next Micro-outcome. Furthermore, we group Micro-outcomes under Clusters (or Domains), and Clusters (or Domains) under Grade Bands via constructing has-parent-of relationships between two given nodes. The resulting network map totals 10 Grade Bands, 5 Domains, 63 Clusters and 775 Micro-outcomes, in addition to 843 has-parent-of and 757 has-prerequisite-of relationships. FIGURE 1 illustrates the network model, and FIGURE 2 shows a zoomed in section of a visualization of the network map.

Our network map represents a structured view of how students move through the Common Core Math curriculum. On top of this base map, we superimpose a layer of learner state: for each Micro-outcome, we mark it as directly disrupted if it was scheduled to be taught during the period of school closures in spring 2020. From directly disrupted Micro-outcomes, we follow the path of incoming has-prerequisite-of relationships to arrive at the entire chain of connected nodes. These nodes represent the downstream Micro-outcomes that will be disrupted as a result of earlier, more fundamental upstream Micro-outcomes.

As one application example, we analyzed the disruptions caused by school closures on March 15, 2020 in Massachusetts, starting from 6th grade. Based on the published syllabus of Cambridge Public Schools, we determined a total of 17 directly impacted Micro-outcomes, 52 downstream impacted Micro-outcomes, spanning a total of seven grades. We discuss the significance of this analysis and demonstrate how our network mapping scales to enable analysis at any grade level and at any point during COVID-19, resulting in high predictive power even in later grade levels. We also give examples of how our work can be used to inform curriculum redesign. Lastly, from a technological perspective, we discuss how our work is built with microservice API design, and thus scales to any number of interventions that needs to leverage the mapped data.

Huang, L., & Bicol, K. M., & Willcox, K. E. (2021, July), Modeling COVID-19 Disruptions via Network Mapping of the Common Core Mathematics Standards Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37513

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