Volume 13

Modeling COVID-19 disruptions via network mapping of the Common Core Mathematics Standards

Luwen Huang ¹ , Kayla Bicol ¹ , Karen E. Willcox ✉ ²

1 MIT Mapping Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

2 The Oden Institute, University of Texas at Austin, Austin, TX, 78712, USA

Abstract

This paper develops a mathematical and computational modeling approach that provides a data-driven platform to address research questions relating to student pathways in K-12 education. Specifically, this paper uses scalable network modeling to create a model of the Common Core Mathematics Standards. The result is an educational map that formally represents the Standards and the relationships among them. This educational map is represented mathematically as a network model that forms the basis for computational graph analytics and visualization to identify Standards and learning pathways of interest. Using the network model, we model the disruption due to COVID-19 related school closures in Spring 2020.

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Integrating Computer Science across Wyoming’s K-12 Curriculum from Inception to Implementation: Analysis Using Systems Theory

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Astrid K Northrup ✉ ¹ , Andrea C Burrows Borowczak ²

1 Engineering and Mathematics, Northwest College, Powell, WY, 82435, USA

2 School of Teacher Education, University of Central Florida, Orlando, FL, 32816, USA

Abstract

In 2001, the Wyoming Supreme Court ruled in State v. Campbell County that every Wyoming K‑12 student has constitutional protection for a fair, complete, and equal education appropriate for the times. This protection results in the universal availability of a “basket of goods,” or a set of educational deliverables guaranteed to each student independent of the resources of individual school districts. In 2019, an executive action by Governor Matt Mead added to the deliverables “the use and understanding of computer science.” His executive action initiated a system in which universal computer science became available to every Wyoming K-12 student by the 2022–2023 school year.

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Implementation of Lessons Learned to Simulation-Based Reflection in a Digital Circuits Course

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Samuel J Dickerson ✉ ¹ , Renee M Clark ²

1 Electrical and Computer Engineering, University of Pittsburgh, PA, Pittsburgh, USA

2 Industrial Engineering, University of Pittsburgh, PA, Pittsburgh, USA

Abstract

A unique method for promoting reflection among engineering students was used in the present study involving a digital circuits course. The method combined computer-based simulation for digital circuit design with reflective-thought prompts after a midterm exam for post-exam analysis and reflection. This method was first implemented in a microelectronics course using the SPICE simulator. Lessons learned from the initial implementation were applied to the digital circuits course. These lessons learned included the need to scaffold students in the use of the simulation tool for reflection, the need to balance frequency of reflection with student workload and fatigue, and question prompts that voluntarily elicit broad thought after a milestone event such as a midterm exam (versus a quiz).

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Generating a Classroom Pulse from Active Windows on Student Computers

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M Jean Mohammadiaragh ✉ ¹ , Christopher B Williams ¹

1 Electrical and Computer Engineering, Mississippi State University

Abstract

With technology embedded in an increasing number of educational contexts, it is prudent to identify ways in which instructors can leverage technology to benefit their pedagogical practices. The purpose of this study was to determine if information about students’ active windows on their personal computers could provide actionable information to inform real-time instructional interventions and post-lecture reflection on practices. The active window approach mitigates issues with prior data collection methods and provides an opportunity to capture complete, real-time student computer usage without the need to install spyware. Based on observing 68 first-year engineering students and 32 second-year engineering students in large engineering lectures, we generated error rates of 4.28% with a 95% confidence interval of [2.81%, 6.04%] in a structured computer use course setting and 6.89% with [4.42%, 10.17%] in a semi-structured use setting.

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Mobile Applications to Measure Students’ Engagement in Learning

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John C Chen ¹ , David Janzen ² , Ryan Chang ³ , Karen Mcgaughey ⁴ , Jim Widmann ⁵

1 Mechanical Engineering, Computer Science and Software Engineering, California Polytechnic State University San Luis Obispo, CA, USA

2 California Polytechnic State University San Luis Obispo, CA, USA

3 Outreach University of Oregon Eugene, OR, USA

4 Mechanical Engineering, Statistics California Polytechnic State University San Luis Obispo, CA, USA

5 California Polytechnic State University San Luis Obispo, CA, USA

Abstract

Evidence-based instruction or active learning is being more widely implemented in college teaching, and there is a need for instructors, evaluators and researchers to quantify their implementation in order to, for example, determine the efficacy of a new instructional technique. Here we introduce a new method for measuring students’ level of engagement with their learning.

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