Abstract
To understand instruction during the spring 2020 transition to emergency distance learning (EDL), we surveyed a sample of instructors teaching undergraduate EDL courses at a large university in the southwest. We asked them how frequently they used and how confident they were in their ability to implement each of nine promising practices, both for their spring 2020 EDL course and a time when they previously taught the same course face-to-face (F2F). Using latent class analysis, we examined how behavioral frequencies and confidence clustered to form meaningful groups of instructors, how these groups differed across F2F and EDL contexts, and what predicted membership in EDL groupings. Results suggest that in the EDL context, instructors fell into one of three profiles in terms of how often they used promising practices: Highly Supportive, Instructor Centered, and More Detached. When moving from the F2F to EDL context, instructors tended to shift “down” in terms of their profile—for example, among F2F Highly Supportive instructors, 34% shifted to the EDL Instructor Centered profile and 30% shifted to the EDL More Detached Profile. Instructors who reported lower self-efficacy for EDL practices were also more likely to end up in the EDL More Detached profile. These results can assist universities in understanding instructors' needs in EDL, and what resources, professional development, and institutional practices may best support instructor and student experiences.
Highlights
DL Experience < -0.01 < -0.01 -0.02 0.08 0.07 0.05 -0.06 -0.05 -0.02 STEM Family Care0.04 0.02 0.01 0.03 0.01 < 0.01 -0.06 -0.02 -0.01 TT Teaching0.17 0.13 0.15 -0.17 -0.17 -0.15 -0.01 0.02 < -0.01 Lecturer F2F Frequency Inst
We examined how reported frequency of and confidence for practices may cluster to form meaningful groups of instructors, how these groups differ across F2F and emergency distance learning (EDL) contexts, and what predicts membership in EDL groupings
To capture background characteristics which may influence teaching practices, we used administrative data to determine previous experience teaching online (DL Experience) and whether the instructor was in a STEM department (STEM), and collected survey data on instructor classification, and whether the instructor reported that needing to care for family impacted their EDL teaching (Family Care)
Summary
To capture background characteristics which may influence teaching practices, we used administrative data to determine previous experience teaching online (DL Experience) and whether the instructor was in a STEM department (STEM), and collected survey data on instructor classification (lecturer, tenure-track teaching faculty, or tenure-track research faculty), and whether the instructor reported that needing to care for family impacted their EDL teaching (Family Care). The nominal class variable would say that this person belongs to Class 1; this does not take into account the 25% of uncertainty We use these classification probabilities to answer our second research question—what predicts membership in EDL frequency profiles. Wald’s F post-hoc tests were used to test for differences between profiles included in the regression Results from these analyses would provide information on which background characteristics, EDL self-efficacy profile, and F2F practices predicted the instructor’s likelihood of being in a given EDL frequency profile compared to both other profiles together. Due to the superior simplicity and interpretability of the OLS models, we present results from OLS models below
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