2 The Characteristics of Dissemination Success (CODS) Model as a Framework for Changing the Culture of Teaching and Learning
Katerina V. Thompson and Gili Marbach-Ad
Theories of change based on existing change models are indispensable tools for planning, implementing, and evaluating the success of change efforts. This chapter describes how a theory of change based on the Characteristics of Dissemination Success (CODS) framework (Bourrie et al., 2014) is being used to guide a multi-departmental effort to create a more collaborative, student-centered culture of teaching and learning within the biological sciences.
There is convincing evidence that students who are actively engaged in constructing knowledge achieve deeper and more durable learning. STEM courses that utilize active learning have higher pass rates (Freeman et al., 2014), which could ultimately lead to greater persistence of students on STEM career trajectories. Thus, wider adoption of these active learning approaches is key to maintaining our scientific research enterprise and cultivating a diverse, scientifically literate workforce (President’s Council of Advisors for Science and Technology [PCAST], 2012).
Despite the wide variety of active learning approaches that have been described in peer reviewed journals, shown to be effective, and championed by esteemed scientists, these approaches are far from widespread in undergraduate STEM classrooms (Dancy & Henderson, 2010; Hazen et al., 2012; Wieman et al., 2010). Attempts to change the culture of teaching to integrate more active learning have largely relied on a somewhat naïve model for the diffusion of innovation, in which increasing faculty awareness of effective teaching practices was thought to be a sufficient impetus to catalyze widespread adoption (Foote, 2014; Henderson et al., 2011; Kezar et al., 2015). Recent research has revealed that faculty are generally familiar with active learning approaches and believe in their superiority over passive approaches, but their behavior in the classroom often does not reflect this belief (Henderson et al., 2012; Marbach-Ad et al., 2012; Marbach-Ad et al., 2014). This may be due in part to the tendency of faculty to overestimate the extent to which their teaching incorporates active learning (Ebert-May et al., 2011), but it is also common for faculty to try out new teaching methods and then abandon them (Henderson et al., 2012) or adapt them in ways that undermine their effectiveness (Henderson & Dancy, 2008). Others never broaden their approaches beyond the traditional lecture.
Many administrative and institutional barriers to the widespread implementation of empirically supported, active learning approaches have been identified, including class size, physical constraints of large lecture halls, lack of support for faculty professional development, institutional pressure for high research productivity, and lack of institutional rewards for effective teaching (Brownell & Tanner, 2012; Kober, 2015; Labov et al., 2009; PCAST, 2012; Seidel & Tanner, 2013; Wieman et al., 2010). STEM faculty members consistently identify student resistance as another major barrier (Bourrie et al., 2014; Henderson & Dancy, 2007; Seidel & Tanner, 2013). Undergraduate students often favor traditional lecturing over active learning (Fagen et al., 2002; Henderson & Dancy, 2007; Weimer, 2002), possibly because active learning methods often require students to exert greater effort in class, meet higher standards, and cope with the uncertainties and risks that come with novel learning environments compared with traditional learning environments (Doyle, 2008; Weimer, 2002). Negative instructor and student attitudes towards active learning can be mutually reinforcing. The risk of negative student evaluations can intensify instructors’ resistance to change, especially when these evaluations influence tenure and promotion (Austin, 2011; Henderson & Dancy, 2007). It is now clear that overcoming the complex barriers to widespread implementation of active learning approaches will require a systems-level approach that engages students, instructors, and the administrative structure in which they operate.
Agencies that fund undergraduate STEM education reform have recognized this need and increasingly expect that proposals for instructional reform be guided by an explicit theory of change. For example, the National Science Foundation’s Improving Undergraduate STEM Education program guidance states
While it is expected that each proposed undergraduate STEM education reform project be tied to a theory of change, there are few models for how theories of change should drive specific project activities. We describe how a theory of change developed specifically for the university context, the Characteristics of Dissemination Success (CODS; Bourrie et al., 2014), provided the framework for improving undergraduate biological sciences education at the University of Maryland.
1.1 Theories of Change
Theories of change are intended to guide the planning, management, and evaluation of interventions (Mayne, 2015). They are particularly useful when promoting change in a complex system, because they require a detailed accounting of 1) the relationships among specific activities, 2) the outcomes that can result from those activities, and 3) the conditions under which the activities produce the desired outcomes. They are similar to other frameworks for project planning, such as causal pathways and logic models, but recognize that the linkage between activity and outcome may only be realized when key assumptions are met. Theories of change strengthen project evaluation by providing a process for identifying critical unmet assumptions in the event that a particular intervention is less effective than expected.
Having a well-reasoned and articulated theory of change is important for the success of STEM education reform efforts. Indeed, in the absence of an explicit theory of change, change agents (who are typically STEM faculty unfamiliar with the scholarly literature on change) are likely to rely on implicit theories of change that are faulty, which undercuts their efforts to catalyze change (Kezar et al., 2015). Kezar et al. (2015) identified several examples of implicit theories of change that are widespread and likely to undermine STEM education reform efforts, including the belief that change efforts must be centered at the level of the department, data alone can motivate change, and change cannot be accomplished without an infusion of funding.
1.2 The CODS Model
CODS is particularly well suited to instructional reform efforts because it explicitly acknowledges the multifactorial nature of decision-making in higher education (Bourrie et al., 2014). The framework emerged from an NSF-funded Delphi study to identify the characteristics that might influence the spread of teaching innovations. It provides a promising theoretical model of institutional change because it explicitly acknowledges the multiple individual and contextual factors that influence an individual’s decision of which teaching methods to employ. The multifactorial nature of this framework implies that change efforts that encompass multiple components of the university educational system are more likely to succeed than more narrowly tailored efforts.
The CODS framework is built on a robust theoretical literature on decision-making (e.g., Ajzen’s  Theory of Planned Behavior). It takes as its starting point Rogers’ (2003) Innovation-diffusion model, which describes how changes can spread across people and organizations. Within the context of teaching, an individual gains knowledge of a new teaching approach and forms an attitude towards it. If that attitude is favorable, the individual may decide to try out the teaching approach. This initial decision can be reinforced following successful implementation, which leads to long-term change in teaching practices (Bourrie et al., 2014).
To this basic model, CODS adds layers of contextual factors that might influence a faculty member’s decision-making process. Under the CODS framework (Figure 1), a faculty member’s use of evidence-based approaches (behavior) is a result of a change in their intentions, which in turn is influenced by their attitudes toward evidence-based teaching approaches, the perceived departmental and disciplinary norms, and the degree to which they believe they are able to successfully implement those approaches. These faculty beliefs are themselves determined by a combination of individual and contextual factors that include characteristics of the students, faculty members, institution, and the teaching approaches under consideration.
2 Institutional Context and Objectives
We applied the CODS model to an effort to increase cohesion between courses in the biological sciences curriculum at the University of Maryland. Specifically, we sought to
- Tighten the linkages between the first four courses in the biological sciences curriculum to help students build a coherent base of knowledge and skills
- Strengthen faculty commitment to active pedagogies by engaging them in an iterative cycle of developing, assessing, evaluating, and refining instructional activities
- Gain student buy-in for learning approaches that require students to be actively engaged and exert greater effort
Creating curricular cohesion had long been a challenge for several reasons. First, the biological sciences major is not situated within a single department, but is instead collaboratively sponsored by three departments: Biology, Entomology, and Cell Biology and Molecular Genetics. It enrolls roughly 1,700 undergraduate students, and more than 90 faculty members teach courses in the program. The program is overseen by a leadership team comprised of a college-level administrator who serves as biological sciences program director and undergraduate directors from each of the departments. The leadership team provides a degree of coordination among instructors, but there are few opportunities for the biological sciences faculty as a whole to discuss teaching-related issues and priorities.
Biological sciences students can choose among five areas of specialization at the upper level, but all complete a sequence of four courses that provide a common foundation for the major (Table 1). Some of these courses are also required or recommended for students in a variety of other science majors and those who intend to apply to graduate programs in the health professions. In addition to this diversity in student majors and career aspirations, there is considerable heterogeneity in academic preparation among students because there are multiple pathways through the curriculum. First, the first-year courses can be taken in either order, as can the second-year courses. In addition, students outside of the biological sciences may opt to take only one of the first-year courses and one of the second-year courses. Thus, there is considerable heterogeneity amongst the students enrolled in a given course.
|Course||Lecture sections/year||Annual enrollment|
|Principles of Evolution and Ecology||9||700|
|Principles of Cell and Molecular Biology||13||1500|
|Principles of Biology III—Organismal Biology||7||700|
|Principles of Genetics||6||850|
Each of the biological sciences departments has multiple faculty members who have been deeply engaged in transforming their courses to make greater use of active learning approaches. Despite their efforts, reform has occurred in somewhat isolated pockets, and the locus of change has primarily been the individual faculty member or individual course. Thus, students encounter active learning sporadically through the curriculum. This has created a situation where some students are hesitant to enroll in courses with active pedagogies. Some are simply unfamiliar with the expectations of these learning environments. Others are quite vocal in their preference for traditional, passive modes of instruction, despite compelling evidence from well-validated diagnostic measures that they are learning much more in the active classroom (E.F. Redish, unpublished data).
3 Using the CODS Framework to Guide Implementation and Assessment Activities
The CODS framework was originally conceptualized as linear, which implies that changes in faculty behavior do not affect other elements of the system (Figure 1). However, we view the change process as recursive in that changes in faculty teaching behaviors may directly affect students’ attitudes and motivation and their own attitudes, as well as the attitudes of their colleagues and even administrators. To account for this recursive nature, we reconceptualized the process as cyclical (Figure 2), which implies that the system will evolve over time. Viewing the process as cyclical allowed us to identify multiple points at which the system might be perturbed, which then guided our choice of interventions and activities.
3.1 Faculty Learning Community
As our core intervention, we established a faculty learning community (FLC) consisting of faculty who taught one or more of the first four courses in the biological sciences curriculum, which serve as a gateway to the major. It was clear that achieving greater cohesion in content and pedagogy would require a cultural shift towards viewing teaching as a collaborative, rather than solitary, endeavor. Learning communities are recognized as a powerful strategy for scaling up the spread of innovations beyond individual early adopters, helping to ensure their widespread impact and long-term sustainability (Kezar, 2011). Importantly, the FLC approach allowed us to simultaneously address multiple levels of the CODS framework, from individual beliefs (e.g., self-efficacy) to context (e.g., departmental climate for teaching) (Figure 2). This approach is congruent with the Four Frames model of systemic change, which emphasizes the need for interventions to take into account people, the power relationships among them, the institutional structures in which they operate, and the symbols of their institutional culture (Reinholz & Apkarian, 2018; Pilgrim, et al., this volume).
We recognized that this collaborative effort would encompass a great deal of diversity in content emphases, teaching styles, and teaching philosophies, even within a single multi-section course. Rather than insisting that all participating faculty subscribe to a particular view or adopt a specific pedagogy, we believed that FLC interactions would cultivate a shared vision that respected faculty expertise and autonomy. We sought for faculty to engage together in a process, rather than achieving a prescribed outcome, an approach that has been touted as promising for achieving institutional change (Henderson et al., 2011; Earl et al., this volume).
3.2 FLC Activities
The FLC met biweekly over a 2.5-year time period to 1) create progressive, active learning activities, 2) engage in an iterative process of assessing and refining instructional activities, and 3) develop metacognitive teaching strategies to help students recognize evidence of their learning to gain student buy-in for approaches that require greater effort and engagement. The biweekly meetings were supplemented with day-long teaching retreats held at the end of each semester, which provided an opportunity for the group to reflect on its progress and set priorities for the upcoming semester. Each retreat also provided an opportunity for the FLC to interact with invited speakers, who introduced a variety of new pedagogical and assessment methods. During selected retreats, faculty members teaching at the upper level were also invited to join in, as a way of propagating the group’s efforts and amplifying culture change.
Each activity undertaken by the FLC was designed to reinforce multiple components of the CODS framework. For example, one recurrent activity during the biweekly meetings and retreats was “Show and Tell,” where members of the FLC shared their teaching strategies for particular learning outcomes. These Show and Tell sessions provided opportunities to influence faculty beliefs about subjective norms by raising awareness of the extent to which active learning was being used by peers. The sessions also could be expected to increase faculty teaching self-efficacy by demonstrating how various approaches could be applied effectively. Furthermore, Show and Tell sessions highlighted characteristics of specific teaching approaches, which helped faculty better understand their feasibility and adaptability. According to the CODS model, these factors collectively influence faculty intentions regarding their choice of teaching approaches.
Another major focus was implementing activities to help students develop their metacognitive skills. During one of our full-day retreats, we held a workshop with an invited speaker from the campus learning assistance center, followed by a panel of science faculty who were currently using metacognitive approaches. We then provided every faculty member with a copy of Nilson’s (2013) Creating Self-Regulated Learners, which contains numerous practical strategies for developing students’ metacognitive skills. Before leaving the workshop, each faculty member created an implementation plan for introducing one or more metacognitive activities into their course. We later surveyed faculty as to which metacognitive strategies they were employing and which strategies they were interested in learning more about. We held a workshop in which experienced faculty shared their strategies, then were matched with those wishing to learn more for further discussion. This effort to increase student metacognition touched on several aspects of the CODS model: it raised awareness of the characteristics of particular teaching approaches, bolstered faculty self-efficacy with those approaches, and had the potential downstream effect of influencing characteristics of students by increasing their awareness of the impact of active pedagogies on their learning.
3.3 Using CODS to Understand Change
Our evaluation strategy was designed to document changes in multiple components of the CODS cycle (e.g., faculty attitudes, faculty behaviors, student attitudes, administrative support for teaching) over the course of the initiative. This process is ongoing, and we share below our strategic evaluation plan.
We are using a mixed methods approach that integrates quantitative and qualitative evidence to evaluate our progress in fostering changes to the culture of teaching and learning. In addition to gathering data on faculty adoption of evidence-based teaching methods, we are examining multiple factors identified in the CODS framework as influencing faculty adoption of new teaching methods, including characteristics of the faculty, their students, and the institutional context in which they operate. Specific tools and approaches are detailed in Table 2.
We surveyed all participating faculty four times over the course of the initiative (in conjunction with teaching retreats). The surveys gathered data on current teaching practices, faculty intentions regarding active pedagogies, their attitudes towards specific educational goals, and individual characteristics thought to influence these (e.g., teaching self-efficacy, receptivity to change). While we originally planned to use surveys to also gather information on institutional factors such as climate for teaching, perceived rewards for teaching excellence, and peer support, we later realized that faculty might hesitate to respond to questions on these sensitive topics. We instead opted to address these issues in semi-structured interviews with participating faculty and administrators.
We simultaneously gathered survey data on student characteristics that might influence faculty attitudes, intentions, and behaviors, such as motivation for learning biology, science self-efficacy, and receptivity to active learning. These surveys were administered each semester in every gateway course (for enrolled students of all majors) and at graduation (for biological sciences majors only).
|Characteristics of students||Engagement||Student Engagement Scale,
cognitive engagement (Gunuc & Kuzu, 2015)
|I try to do my best during class.|
|Self-efficacy||Science Self-efficacy survey (Estrada et al., 2011)||I am confident that I can generate a research question to answer.|
|Motivation to learn||Patterns of Adaptive Learning Scales (Hernandez et al., 2013)||An important reason why I do my work in school is because I want to get better at it.|
|Receptivity to change||Hercovitch & Meyer (2002)||I believe in the value of this change [to greater use of active learning].|
|Characteristics of faculty
|Behavior||Postsecondary Instructional Practices survey (Walter et al., 2016)||I use student assessment results to guide the direction of my instruction during the semester.|
|Attitudes and intentions||Science Teaching Beliefs and
Practices survey (Marbach-Ad et al., 2014)
|Rate the importance of the following approaches to teaching undergraduate students: Using a variety of teaching methods|
|Receptivity to change||Hercovitch & Meyer (2002)||I believe in the value of this change [to greater use of active learning].|
|Value of teaching in relation to research||Structured interviews||How do you balance competing responsibilities (e.g., teaching, advising, and research)?|
|Self-efficacy||College Teaching Self-Efficacy Scale (Prieto-Navarro, 2005)||How confident are you in your ability to create a positive classroom climate for learning?|
|Sense of Community Index 2
(Chavis et al., 2008)
|People in this community have similar needs, priorities, and goals.|
|Rewards for innovation||Structured interviews|
In designing the evaluation plan, we paid particular attention to characteristics that were identified by the CODS Delphi study as being highly influential (Bourrie et al., 2014). This resulted in the inclusion of constructs that are not usually considered in studies of faculty teaching. For example, receptivity to change was identified as an important influence on faculty, students, and administrators. For this construct, which became part of the evaluation measures for both students and faculty, we adapted items that were originally developed for workplace change efforts (Hercovitch & Meyer, 2002).
Our preliminary analyses (Marbach-Ad et al., 2019) indicated that one of the strengths of the FLC was that it contained some more-experienced and confident “innovative teachers” that inspired and assisted those who have less experience or confidence with active pedagogies. While participation in the FLC did not change the teaching self-efficacy of the most confident instructors, it did appear to boost the confidence of those who were initially less confident. Faculty reflected that through their interactions with other FLC members, they learned new teaching and assessment techniques and ways of promoting student metacognition. They also expressed a desire for more cross-course interactions (e.g., sharing syllabi, observing each other’s classes, building a library of student-centered activities, collaborating to create assessment questions), indicating a shift towards a more collaborative culture of teaching.
Using the CODS framework to guide project planning has enabled us to develop professional development activities that foster a supportive community around teaching, bolster faculty self-efficacy in teaching, and raise awareness of the changing departmental norms regarding use of active pedagogies. We suspect that these coordinated efforts will have a positive impact on student engagement and motivation to learn, although it is still too early to measure that impact. Using the CODS model as an evaluation framework has enabled us to collect the data necessary to construct a comprehensive understanding of the current culture to teaching and learning in the biological sciences. As we continue to collect and analyze our data, this approach will help us identify strategies that can be used as levers for institutional change and points in the process where additional interventions might be necessary. As such, this theory of change is a powerful tool in shaping institutional culture in support of more effective teaching.
5 About the Authors
Katerina V. Thompson is the Assistant Dean for Science Education Initiatives in the College of Computer, Mathematical, and Natural Sciences at the University of Maryland.
Gili Marbach-Ad is the Director of the Teaching and Learning Center (TLC) in the College of Computer, Mathematical, and Natural Sciences at the University of Maryland.
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