Online grading platforms: Establishing a connection between course coordination and local data

Martinez, Antonio Estevan; Pilgrim, Mary E.

doi:10.1080/0020739x.2021.1937731

Cited by 1 publication

(2 citation statements)

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“…Instructors state that it is easier to integrate research-based methods if other instructors are implementing new methods at the same time (Foote et al, 2016;Henderson & Dancy, 2007); implementation requires supportive departmental and institutional mentors (Henderson & Dancy, 2007;Shadle et al, 2017). Coordinating a course across multiple class sections is a possible route to execute this change and offers an opportunity for instructional support outside of formal avenues of professional development (Apkarian et al, 2019;Golnabi et al, 2021;Martinez & Pilgrim, 2021;Williams et al, 2021).…”

Section: Shared Decision On Instructional Methodsmentioning

confidence: 99%

“…Visitors, instructors, teaching assistants, adjuncts, and lecturers (VITAL; Levy, 2019) that teach sections of coordinated mathematics courses have access to course coordinators to discuss pedagogical approaches and active learning activities (Golnabi et al, 2021). In addition, coordinators of active learning courses in mathematics have also been shown to utilize local data (e.g., student performance data, grades in subsequent courses, and student-generated data) to inform curriculum and pedagogy (Martinez & Pilgrim, 2021). In physics, co-teaching between a new instructor and an experienced instructor that uses active learning showed immediate uptake of these teaching practices by the new instructor and a positive shift in their beliefs and intentions of using these strategies in the future .…”

Section: Classroom Contextual Factorsmentioning

confidence: 99%

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Evaluating the impact of malleable factors on percent time lecturing in gateway chemistry, mathematics, and physics courses

et al. 2022

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Background Active learning used in science, technology, engineering, and mathematics (STEM) courses has been shown to improve student outcomes. Nevertheless, traditional lecture-orientated approaches endure in these courses. The implementation of teaching practices is a result of many interrelated factors including disciplinary norms, classroom context, and beliefs about learning. Although factors influencing uptake of active learning are known, no study to date has had the statistical power to empirically test the relative association of these factors with active learning when considered collectively. Prior studies have been limited to a single or small number of evaluated factors; in addition, such studies did not capture the nested nature of institutional contexts. We present the results of a multi-institution, large-scale (N = 2382 instructors; N = 1405 departments; N = 749 institutions) survey-based study in the United States to evaluate 17 malleable factors (i.e., influenceable and changeable) that are associated with the amount of time an instructor spends lecturing, a proxy for implementation of active learning strategies, in introductory postsecondary chemistry, mathematics, and physics courses. Results Regression analyses, using multilevel modeling to account for the nested nature of the data, indicate several evaluated contextual factors, personal factors, and teacher thinking factors were significantly associated with percent of class time lecturing when controlling for other factors used in this study. Quantitative results corroborate prior research in indicating that large class sizes are associated with increased percent time lecturing. Other contextual factors (e.g., classroom setup for small group work) and personal contexts (e.g., participation in scholarship of teaching and learning activities) are associated with a decrease in percent time lecturing. Conclusions Given the malleable nature of the factors, we offer tangible implications for instructors and administrators to influence the adoption of more active learning strategies in introductory STEM courses.

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