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Abstract Background 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. Results 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. Conclusions 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.

Abstract 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.

Abstract Background Team-based instructional change is a promising model for improving undergraduate STEM instruction. Teams are more likely to produce sustainable, innovative, and high-quality outcomes than individuals working alone. However, teams also tend to involve higher risks of failure and can result in inefficient allocation of valuable resources. At this point, there is limited knowledge of how teams in the context of STEM higher education should work to achieve desirable outcomes. Results In this study, we collect semi-structured interview data from 23 team members from a total of 4 teams at 3 institutions across the USA. We analyze the results using a grounded theory approach and connect them to the existing literature. This study builds upon the first part of our work that developed a model of team inputs that lead to team outcomes. In this part, we identify the mechanisms by which input characteristics influence teamwork and outcomes. Team member data expand this initial model by identifying key aspects of team processes and emergent states. In this paper, we present five team processes: strategic leadership, egalitarian power dynamics, team member commitment, effective communication, and clear decision-making processes, that shape how teams work together, and three emergent states: shared vision, psychological safety, and team cohesion, that team members perceived as important aspects of how teams feel and think when working together. Conclusions This work furthers our understanding of how instructional change teams can be successful. However, due to the highly complex nature of teams, further investigation with more teams is required to test and enrich the emerging theory.

Abstract Background Instructional change efforts involving teams of change agents are becoming increasingly prevalent at higher education institutions across the US. Teams may be able to make more lasting and high-quality changes to STEM courses than instructors working alone. But team-based change efforts are also risky. They tend to require more resources than individually focused efforts, and there are many ways in which teams can fail to achieve their goals. So far, research on how change leaders can best support instructional change teams has been extremely limited. We lack a context-specific model that can help us to understand how, when, and why instructional change teams are likely to generate positive outcomes. This study begins to address this limitation by exploring the decisions that project leaders make when establishing supports for instructional change teams. Results We use a grounded theory approach to analyze interviews with 28 leaders of team-based instructional change projects and connect our findings to existing literature about teams in other contexts. We identify five categories of key team inputs: the nature of the task, who participates, process constraints, external engagement, and access to resources. We situate these team inputs in a preliminary model of instructional change teams. In our model, we posit that these team inputs are consequential for how teams work together and for how teams think and feel about their work. This in turn leads to various kinds of team outcomes. Conclusions Our current model lays out key decisions that project leaders make when setting up instructional change teams. It also highlights the mechanisms by which instructional change team inputs can lead to various outcomes. We argue that this can guide change leaders to have productive conversations when initiating new teams or troubleshooting collaboration challenges of existing teams. Our research also provides a foundation for deeper exploration into how teams work together and the consequences for team outcomes. In particular, future research focused on the perspectives of team members could test and build on this initial model.

Abstract Background Social network analysis (SNA) literature suggests that leaders should be well connected and can be identified through network measurements. Other literature suggests that identifying leaders ideally involves multiple methods. However, it is unclear using SNA alone is sufficient for identifying leaders for higher education change initiatives. We used two sets of data, teaching discussion network data taken at three different times and respondent nominations for leaders, to determine whether these two methods identify the same individuals as leaders. Results Respondent-nominated leaders have more direct and indirect ties on average than non-leaders, which aligns with the SNA literature. However, when looking at individuals as leaders, many respondent-nominated leaders would not be identified using SNA because they are poorly connected. Also, many individuals who were not nominated would have been considered leaders because they are well connected. Further examining these results did not indicate why there is such a difference between the SNA-identified and respondent-nominated leaders. Conclusions While these two methods identify some of the same individuals as leaders, there are many differences between the two methods. Using just one method may not be adequate for ensuring that suitable individuals are selected to lead these projects. We recommend multiple methods when selecting leaders.