Estimating a Dose-Response Relationship in Quasi-Experimental Student Success Studies

Shao, Lucy; Levine, Richard A.; Guarcello, Maureen A.; Wilke, Morten C.; Stronach, Jeanne; Frazee, James P.; Fan, Juanjuan

doi:10.1007/s40593-021-00280-0

Cited by 1 publication

(1 citation statement)

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“…We have a discrete treatment and class size and must resort to so‐called generalized propensity scores. We have extended the CBGPS approach (Fong et al, 2018) to an educational data mining setting (Shao et al, 2022) and thus used this method here to quantify a treatment effect. We note that our current area of research is extending CBGPS to multinomial outcomes and multiple treatments.…”

Section: Methodsmentioning

confidence: 99%

A learning analytics case study: On class sizes in undergraduate writing courses

Levine

Rivera

et al. 2023

Stat

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With the collection and availability of data on student academic performance and academic background, higher education institutions have recently stepped up initiatives in and infrastructure for learning analytics, leveraging this deluge of data to inform student success. With definitions of student success varying from analyses of what predicts levels of specific career readiness competencies to degree completion, the environment is a fertile ground for statistical practice and collaboration among a statistically savvy yet diverse clientele of instructors, programme advisors and administrators. In this paper, we discuss our experiences to this end through a consulting project evaluating the impact of writing course class size on students achieving a graduation writing requirement. In detailing the workflow for and challenges in this project, we share aspects of statistical communication and reporting, applications of innovative statistical methodology developed by our research group for handling confounding factors and correlated inputs and training through an interdisciplinary applied institutional research professional development programme. This paper illustrates how instilling an appreciation for statistical inference through each of these components is invaluable for capturing institutional buy‐in for data‐informed decision‐making in general statistical practice.

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Section: Methodsmentioning

confidence: 99%