Traditional approaches to teaching and learning in large undergraduate STEM lectures has not kept pace with the massification and diversification of higher education. Efforts to alleviate learning obstacles call for improved instruction, stipulating the utility of specific pedagogical techniques delineated as “promising practices”; however, little evidence supports their effectiveness. In this study, a quasi-experimental method is applied to a large panel dataset containing course observations and institutional records to investigate the effect of promising practices on student learning. Additionally, differential effects are examined across several important nontraditional and historically under-served populations, such as Hispanic, first-generation, low-income and lower prior ability students. Results suggest that two information relaying techniques—the use of prior content and reviewing exam content—were positively and significantly associated with student outcomes, and estimates were found to be stable across subgroups. Implications for higher education teaching and learning, as well as empirical research in these areas, are discussed.
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Epistemology is here used to refer to an explicit body of instructional techniques. It is not in reference to a particular theory or theories of knowledge; neither is it in reference to philosophical study.
Active learning is often used in two ways: (1) In reference to, and synonymous with, learner-centered teaching, and (2) with respect to an explicit body of instructional techniques. From this point forward, I use it in the latter sense.
More of the justification for using subsequent course grade is provided in the measures section.
The use of promising practices varies by course and instructor—not just the course level. For instance, two different instructors can teach the same course, or the same instructor can teach the same course (across different terms or sections), and each will have different promising practice implementations. This variation in promising practices at the instructor level allows for the control of instructor-level fixed effects, which controls for invariant features of the instructor (e.g., race, ethnicity, previous background, etc.).
The empirical distribution for this variable was not ideally normally distributed. However, the current methodological and statistical modeling literature suggests that modeling outcome variables as normal (Gaussian) even when the empirical distribution in the sample isn’t remains justified if current scientific knowledge does not suggest that the population distribution is otherwise (McElreath, 2020, p. 81). Additionally, Li, Wong, Lamoureux, and Wong (2012) demonstrate that, “…in a large sample, the use of a linear regression technique, even if the dependent variable violates the ‘normality assumption’ rule, remains valid.” And Schmidt and Finan (2018) note that such transformations are not necessary in large samples and can actually bias estimates. Still, to test the robustness of our results, we provide regression results for three transformations on the dependent variable in the appendix. The consistency of the estimates remain largely stable, with the general message taken from both analyses agreeing. However, due to the literature-based rationale just mentioned, we choose to focus the reporting on the original, non-transformed variable.
I would like to thank the anonymous journal reviewer for bringing this potential to such focus.
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I would like to thank Mark Warschauer and Di Xu for providing the data and guidance that made this project possible.
National Science Foundation under Grant Number 1256500.
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Orona, G.A. Broken Promises? Examining the Effectiveness of Promising Practices in STEM Lectures by Student Subgroups. Innov High Educ 46, 223–239 (2021). https://doi.org/10.1007/s10755-020-09536-4
- Higher education
- Promising practices
- Undergraduate education
- Fixed effects