Faculty Beliefs about Intelligence Are Related to the Adoption of Active-Learning Practices

Abstract

Mounting evidence of the efficacy of active learning has prompted educators to consider adoption of these practices in college-level classrooms. One tenet of active learning is that most, if not all, students have the ability to learn. Instructors’ perspectives on learning, however, may or may not be aligned with this. One belief held by some educators is that intelligence is fixed, that is, some students are more intelligent and have a higher ability to learn than others. Instructors with a fixed mindset may not be convinced that their investment in developing active-learning materials will be as fruitful as the education evidence suggests, because these instructors may not believe that most students can grow in their learning. Here, we explored the relationship between fixed mindsets and the adoption of active-learning strategies. We found that instructors with higher fixed mindsets were less persuaded that active-learning strategies were a good idea and less likely to implement the teaching practices. Our research suggests that development initiatives should explicitly address educators’ lay theories of intelligence (fixed or growth mindset) to support successful implementation of active learning.

Supports: A Key Factor in Faculty Implementation of Evidence-Based Teaching

“…, 2016; Aragón et al. , 2016, 2018). Many participants return to the SIs to facilitate future trainings, and many have gone on to run smaller-scale versions of the SI at their own institutions.…”

“How Well Does Your Structural Equation Model Fit Your Data?”: Is Marcoulides and Yuan’s Equivalence Test the Answer?

“…Interpreting these complex relationships quantitatively requires the application of multivariate statistical tools capable of predicting one or more outcomes through multiple possible pathways (e.g., mediation, moderation). For this reason, structural equation model analyses have become common in life sciences education research (e.g., Aragón et al, 2018;Corwin et al, 2018;Estrada et al, 2018) to explain complex sequential relationships among several analytical variables. Structural equation modeling (SEM) is ideally suited for analytical models involving the testing, for example, of multiple mediated pathways (Taylor et al, 2008;Williams and MacKinnon, 2008).…”

Recent Research in Science Teaching and Learning

“…Although Canning and colleagues could not conclude specifically what students meant by these practices, it could be that these faculty use less active learning. This finding was observed in another study on faculty mindset that demonstrated that life science faculty with a fixed mindset were less likely to believe the evidence for active learning and less likely to employ it (Aragón et al , 2018).…”