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There has been a growing interest in characterizing factors influencing teaching decisions of science, technology, engineering, and mathematics (STEM) instructors in order to address the slow uptake of evidence-based instructional practices (EBIPs). This growing body of research has identified contextual factors (e.g., classroom layout, departmental norms) as primary influencers of STEM instructors’ decision to implement EBIPs in their courses. However, models of influences on instructional practices indicate that context is only one type of factor to consider. Other factors fall at the individual level such as instructors’ past teaching experience and their views on learning. Few studies have been able to explore in depth the role of these individual factors on the adoption of EBIPs since it is challenging to control for contextual features when studying current instructors. Moreover, most studies exploring adoption of EBIPs do not take into account the distinctive features of each EBIP and the influence these features may have on the decision to adopt the EBIP. Rather, studies typically explore barriers and drivers to the implementation of EBIPs in general. In this study, we address these gaps in the literature by conducting an in-depth exploration of individual factors and EBIPs’ features that influence nine future STEM instructors’ decisions to incorporate a selected set of EBIPs in their teaching.

We had hypothesized that the future instructors would have different reasoning to support their decisions to adopt or not Peer Instruction and the 5E Model as the two EBIPs have distinctive features. However, our results demonstrate that instructors based their decisions on similar factors. In particular, we found that the main drivers of their decisions were (1) the compatibility of the EBIP with their past experiences as students and instructors as well as teaching values and (2) experiences provided in the pedagogical course they were enrolled in.

This study demonstrates that when considering the adoption of EBIPs, there is a need to look beyond solely contextual influences on instructor’s decisions to innovate in their courses and explore individual factors. Moreover, professional development programs should leverage their participants past experiences as students and instructors and provide an opportunity for instructors to experience new EBIPs as learners and instructors.

It is well established in the literature that active learning instruction in introductory STEM courses results in many desired student outcomes. Yet, regular use of high-quality active learning is not the norm in many STEM departments. Using results of a national survey, we identified 16 departments where multiple instructors reported using high levels of active learning in their introductory chemistry, mathematics, or physics courses. We conducted interviews with 27 instructors in these 16 departments to better understand the characteristics of such departments.

Using grounded theory methodology, we developed a model that highlights relevant characteristics of departments with high use of active learning instruction in their introductory courses. According to this model, there are four main, interconnected characteristics of such departments: motivated people, knowledge about active learning, opportunities, and cultures and structures that support active learning. These departments have one or more people who are motivated to promote the use of active learning. These motivated people have knowledge about active learning as well as access to opportunities to promote the use of active learning. Finally, these departments have cultures and structures that support the use of active learning. In these departments, there is a positive feedback loop that works iteratively over time, where motivated people shape cultures/structures and these cultures/structures in turn increase the number and level of commitment of the motivated people. A second positive feedback loop was found between the positive outcome of using active learning instruction and the strengthening of cultures/structures supportive of active learning.

According to the model, there are two main take-away messages for those interested in promoting the use of active learning. The first is that all four components of the model are important. A weak or missing component may limit the desired outcome. The second is that desired outcomes are obtained and strengthened over time through two positive feedback loops. Thus, there is a temporal aspect to change. In all of the departments that were part of our study, the changes took at minimum several years to enact. While our model was developed using only high-use of active learning departments and future work is needed to develop the model into a full change theory, our results do suggest that change efforts may be made more effective by increasing the robustness of the four components and the connections between them.

Many institutional and departmentally focused change efforts have sought to improve teaching in STEM through the promotion of evidence-based instructional practices (EBIPs). Even with these efforts, EBIPs have not become the predominant mode of teaching in many STEM departments. To better understand institutional change efforts and the barriers to EBIP implementation, we developed the Cooperative Adoption Factors Instrument (CAFI) to probe faculty member characteristics beyond demographic attributes at the individual level. The CAFI probes multiple constructs related to institutional change including perceptions of the degree of mutual advantage of taking an action (strategic complements), trust and interconnectedness among colleagues (interdependence), and institutional attitudes toward teaching (climate).

From data collected across five STEM fields at three large public research universities, we show that the CAFI has evidence of internal structure validity based on exploratory and confirmatory factor analysis. The scales have low correlations with each other and show significant variation among our sampled universities as demonstrated by ANOVA. We further demonstrate a relationship between the strategic complements and climate factors with EBIP adoption through use of a regression analysis. In addition to these factors, we also find that indegree, a measure of opinion leadership, correlates with EBIP adoption.

The CAFI uses the CACAO model of change to link the intended outcome of EBIP adoption with perception of EBIPs as mutually reinforcing (strategic complements), perception of faculty having their fates intertwined (interdependence), and perception of institutional readiness for change (climate). Our work has established that the CAFI is sensitive enough to pick up on differences between three relatively similar institutions and captures significant relationships with EBIP adoption. Our results suggest that the CAFI is likely to be a suitable tool to probe institutional change efforts, both for change agents who wish to characterize the local conditions on their respective campuses to support effective planning for a change initiative and for researchers who seek to follow the progression of a change initiative. While these initial findings are very promising, we also recommend that CAFI be administered in different types of institutions to examine the degree to which the observed relationships hold true across contexts.