Network assessment and modeling the management of an epidemic on a college campus with testing, contact tracing, and masking

There remains a great challenge to minimize the spread of epidemics, especially in high-density communities such as colleges and universities. This is particularly true on densely populated, residential college campuses. To construct class and residential networks data from a four-year, residential liberal arts college with 5539 students were obtained from SUNY College at Geneseo, a rural, residential, undergraduate institution in western NY, USA. Equal-sized random networks also were created for each day. Different levels of compliance with mask use (none to 100%), mask efficacy (50% to 100%), and testing frequency (daily, or every 2, 3, 7, 14, 28, or 105 days) were assessed. Tests were assumed to be only 90% accurate and positive results were used to isolate individuals. The effectiveness of contact tracing, and the effect of quarantining neighbors of infectious individuals, was tested. The structure of the college course enrollment and residence networks greatly influenced the dynamics of the epidemics, as compared to the random networks. In particular, average path lengths were longer in the college networks compared to random networks. Students in larger majors generally had shorter average path lengths than students in smaller majors. Average transitivity (clustering) was lower on days when students most frequently were in class (MWF). Degree distributions were generally large and right skewed, ranging from 0 to 719. Simulations began by inoculating twenty students (10 exposed and 10 infectious) with SARS-CoV-2 on the first day of the fall semester and ended once the disease was cleared. Transmission probability was calculated based on an R0 = 2.4. Without interventions epidemics resulted in most students becoming infected and lasted into the second semester. On average students in the college networks experienced fewer infections, shorter duration, and lower epidemic peaks when compared to the dynamics on equal-sized random networks. The most important factors in reducing case numbers were the proportion masking and the frequency of testing, followed by contact tracing and mask efficacy. The paper discusses further high-order interactions and other implications of non-pharmaceutical interventions for disease transmission on a residential college campus.

Developing a Validity Argument for an Inference-Making and Reasoning Measure for Use in Higher Education

The Inference-Making and Reasoning in Biology (IMRB) measure is an assessment tool intended to 1) aid university personnel in advising students on course enrollment, 2) identify students in need of interventions to increase their reasoning skills and likelihood of completing STEM majors, 3) support instructors in determining growth in students’ reasoning skills, and 4) provide a measuring tool to gauge success of higher-education interventions intended to increase reasoning skills. Validity arguments for these four uses of the IMRB are provided by implementing a validity argument approach. This work exemplifies the advantages of framing validation studies within a validity argument framework.

Student Performance in Online Classes at a Hispanic-Serving Institution: A Study of the Impact of Student Characteristics in Online Learning

As online enrollment increases in the United States, it is important to understand the impact of course modality on student outcomes. In particular, there has been limited research on the effect of course enrollment at Hispanic-serving institutions (HSI). The current study evaluated the effect of online course enrollment on student grades and on student withdrawal rates by comparing outcomes in online and face-to-face classes. The main innovation of this study is to use propensity score analysis to control for 15 different student characteristics as a way to control for the selection bias introduced when students self-select into different course modalities. The study used data from a large, public, HSI in the mountain west during the 2017-2018 and 2018-2019 academic years. Baseline results on a two-sample t-test indicated that online students have significantly higher course grades, and non-significantly different withdrawal rates before controlling for student characteristics. The study used a propensity score analysis (PSA) to control for 15 confounding covariates after testing three different PSA models: near-neighbor matching, Mahalanobis’ metric, and optimal matching. After evaluating each model for validity and sensitivity, a near-neighbor 1:2 matching PSA shows a non-significant difference in student grades, and higher withdrawal rates in online classes than face-to-face classes. Given these results, institutions should ensure that they are providing adequate academic support for online students to improve retention and success rates for online students.

Dr D’Angelo’s hidden chord (three utopias?)

In this article we attempt to envision what utopian higher education could be given the realities that currently shape students’ experiences. Postsecondary education is fraught with admissions that favor those with social, cultural, and economic capital; with course enrollment, class size, and instructor accessibility governed by bureaucratic labyrinths and austerity measures; and with major and career selection constrained by looming student debt. As higher education perpetually reproduces social stratification and increasingly mimics corporate practices, it is imperative to ask who and what higher education is for. Why do we continue to engage in it? Are there ways to salvage it from within, or does it require demolishing, clearing the rubble, and building from scratch? Can a utopian version of higher education exist at all if wider social relations endure? Using speculative fiction, this piece grapples with re-envisioning higher education as students’ daily lives, and the world at large, remain within the confines of capitalist realism.

Managing campus parking demand through course scheduling – an approach to campus sustainability

Purpose The purpose of this study is to use the optimization modeling method to explore whether there is an ideal arrangement of course enrollments that can yield optimal parking demand and supply on college campuses. Design/methodology/approach Using the University of Louisville as a case study, this study deploys a three-step analytical process to examine the correlation between parking demand and course enrollment, estimate parking demand based on course enrollment with regression analyses and embed this estimated relationship in an optimization model that minimizes on-campus parking demand and supply. Findings The correlation analyses suggest significant correlations between course enrollments and on-campus parking. The correlation patterns are different between students and university employees. The optimization results indicate that coupling parking supply and course scheduling decisions can reduce parking supply by 30%. Originality/value Voluminous studies on sustainable campus transportation have focused on transportation demand management strategies. The relationship between course-scheduling and parking demand was not explicitly accounted for in most studies. This study's results reveal that parking demand on campus depends on the number of courses offered across time. Thus, factoring and optimizing course schedules in campus parking decisions remains a viable and essential option to reduce on-campus parking demand.

COMPARISON OF FUTURE COURSE ENROLLMENT AMONG STUDENTS COMPLETING ONE OF FOUR DIFFERENT INTRODUCTORY STATISTICS COURSES

A central challenge of introductory statistics is the development of curricula that not only serve diverse students, but also leave them wanting more. To evaluate the potential impact of a multidisciplinary, project-based introductory statistics course, students’ future course decisions were compared against traditional statistics courses using administrative data from the fall 2009 through spring 2018 semesters. Results indicated that the project-based course helped promote continued interest in the field of statistics and data analysis based on subsequent selection of courses in the field. First published December 2020 at Statistics Education Research Journal: Archives

Still a Small World? University Course Enrollment Networks before and during the COVID-19 Pandemic

In normal times, the network ties that connect students on a college campus are an asset; during a pandemic, they can become a liability. Using pre-pandemic data from Cornell University, Weeden and Cornwell showed how co-enrollment in classes creates a “small world” network with high clustering, short path lengths, and multiple independent pathways connecting students. In this paper, we show how the structure of the enrollment network changed as Cornell, like many American colleges and universities, shifted to a hybrid instructional model with some courses online and others in person. Under this model, about half of students are disconnected from the in-person co-enrollment network. In this network, paths lengthened, the share of student pairs connected by three or fewer degrees of separation declined, and clustering increased, with a greater share of ties occurring between students in the same field. The small world became both less connected and more fragmented. (Corrected page proofs. Paper is forthcoming in Sociological Science.)