Qualitative Procedures

First, data was classified into categories on the Activity System Frameworks, using content analysis and constant-comparative structural coding (e.g., see Saldana, 2013). From the case study analysis protocol (e.g., see Yin, 2014) we looked for patterns of variables and analyzed the likelihood of designated outcomes based on the qualitative variable patterns. Researchers used a concept-matching approach (Kane & Trochim, 2007) to categorize the Activity System elements (rules/policies, community, tools, roles) into membership in common variable sets. Next, researchers analyzed the set-membership across case study sites (n=7) in relation to designated outcomes (in this case, STEM-foundational thinking and instructional activities, student achievement growth, both generally and for subgroups of students; and social-emotional supports of students in classrooms, as indicated by the student perception surveys). This “fuzzy-set qualitative comparative analysis” method (Ragin, 2000; Ragin, 2008) allowed researchers to assess which context and activity system variable combinations improved the likelihood of particular outcomes of interest (e.g.: STEM-foundational thinking). Finally, this, combined with the Activity System qualitative analysis, which looks within and across the “triangle” framework for conflicts among variables allowed researchers to highlight potential levers for systemic change within each of the urban elementary schools and classrooms. All survey responses were coded utilizing the appropriate scale and entered into SPSS for analysis. Reports were generated through SPSS to identify key findings in the quantitative data.

Conclusion

This study examines the relationship between STEM foundational thinking and instructional activities present in the Colorado elementary schools. Qualitative and quantitative data were collected and analyzed during the fall semester of 2015 through the spring semester of 2016 from seven participating schools and analyzed in order to answer the research question. Participants included teachers, instructional staff and school leaders, who participated in the Effective Learning Teacher Survey (ELTS), and the Effective Learning Leader Survey (ELLS). Extant data examined included the framework for effective teaching (LEAP) data, Student Perception Survey (SPS) results, the Colorado Department of Education (CDE) School Performance Framework (SPF), and each school’s Unified Improvement Plans (UIP), relevant trend (qualitative data), and archival structure data (e.g.: school schedules and team and committee workflows). An analysis of variance (ANOVA) and a principal components analysis (PCA) were utilized to find relationships and patterns among the variables. The purpose of this research was to ascertain what practices are in place for recruiting and engaging students of color in STEM curricula, as well as recommendations for creating a culturally relevant school culture (e.g.: an effective learning organizations). The findings of this study will contribute toward an understanding of how best to integrate STEM-foundational thinking and instructional activities into mainstream classroom curricula, so as to provide increased access and opportunity for traditionally underperforming students of color.

Final Instrumentation (Effective Learning Teacher Survey for STEM-related Outputs)

The Effective Learning Teacher Survey (ELTS) was co-designed with C-PEER and doctoral candidates/researchers as a way to study comparative perspectives on teaching and learning in public schools. For STEM-related outputs, survey constructs were adapted from the Teaching and Learning International Survey, looking at STEM-foundational thinking and instructional activities through the lens of teachers. Researchers wanted to examine how STEM-foundational thinking is perceived and implemented in elementary schools and classrooms. Teacher survey questions were split into two survey blocks. Questions were randomized in order to improve the survey’s validity and reliability. The use of two survey blocks decreased the amount of time required by participants so that the survey could be completed 20 minutes or less. Each ELTS block (see Fig. 15) is approximately 40 questions. Survey questions related to STEM-foundational thinking utilized a five-point Likert Scale where 1 is “Almost Neve,” and 5 is “Almost Daily” (see Fig. 15). Teachers who took the survey were de-identified through the use of a participant constructed, confidential identification code, which allowed researchers to connect responses across schools without providing any identifiable personal information. Instructions for completion of the survey were accessed by the subjects at a specified URL. The results of the survey were compiled and analyzed by C-PEER.

These features of the ELTS need to be taken into account when interpreting the results. For example, while teacher responses offer insight to the culture of a building, they are still subjective data points akin to interviewing individual teachers. C-PEER and researchers took great care in designing this instrument in order to ensure that the data are reliable and valid across elementary schools.

Effective Learning Leader Survey

The Effective Learning Leader Survey (ELLS) was also co-designed with C-PEER and doctoral candidates/researchers as a way to better understand the role that participation in teacher leadership networks plays in supporting and retaining effective teachers in urban schools. Researchers wanted to understand how opportunities for collaboration and leadership (within and beyond the classroom) can increase teacher efficacy and effectiveness for STEM-foundational thinking, while improving the retention of highly effective teachers. The ELLS was designed to be completed in less than 20 minutes, and is approximately 30 questions (see Fig. 16). ELLS questions related to the STEM-foundational thinking and instructional activities utilized a four point Likert Scale, (1 is “Not at all true,” and 4 is “Very much like my school”), as well as a text response and multiple choice. The ELLS was also administered online using the Qualtrics Survey Software. Participants who took the survey were de-identified through the use of a participant constructed, confidential identification code, which allowed researchers to connect responses across schools without providing any identifiable personal information. Instructions for completion of the survey were accessed by the subjects at the provided URL. The results of the survey were compiled and analyzed by C-PEER.

Student Perception Survey

The Student Perception Survey (SPS) is designed to provide important feedback regarding teacher behaviors and the classroom environment. SPS results can point to strengths and opportunities for greater growth for teachers’ pedagogical practice. I focused primarily on how students perceived STEM-foundational instructional activities in the classroom. The survey (see Table 1) is comprised of 30 questions and can be administered in 45 minutes. According to the school district’s LEAP Handbook, “the SPS is administered once per year in the late fall to students in grades 3-12,” in order for administration and teachers to use results from the survey to make adjustments to instructional practices (LEAP, 2015). Responses are scored on a four point Likert Scale where 1 is “Never,” and 4 is “Always.” For this focus-study, the responses for items under the SPS construct of Facilitates Learning were analyzed. The results of the survey were compiled by the school district. Results for the participant schools provided by the district were analyzed for variance and correlation by C-PEER. The STEM-foundational thinking questions were derived a variety of sources (see Technical Report) (Seidel, et al. 2016).

LEAP Framework for Effective Teaching

Researchers specific to analyzing STEM-foundational thinking, examined results for teachers in participating schools on nine indicators on the framework for effective teaching (LEAP, 2015). From the Framework for Effective Teaching Observation Domain, these include: 

• LE.1: Demonstrates knowledge of, interest in and respect for diverse students’’ communities and cultures in a manner that increases equity (Positive Classroom Culture and Climate).
• LE.3: Implements high, clear expectations for students’ behavior and routines (Effective Classroom Management).
• I.1: Clearly communicates the standards-based content-language objective(s) for the lesson, connecting to larger rationale(s) (Masterful Content Delivery).
• I.2: Provides rigorous tasks that require critical thinking with appropriate digital and other supports to ensure students’ success (Masterful Content Delivery).
• I.3: Intentionally uses instructional methods and pacing to teach the content-language objective(s) (Masterful Content Delivery).
• I.4: Ensures all students’ active and appropriate use of academic language (Masterful Content Delivery).
• I.5: Checks for understanding of content-language objective(s) (High-Impact Instructional Moves).
• I.6: Provides differentiation that addresses students’ instructional needs and supports mastery of content-language objective(s) (High-Impact Instructional Moves).
• I.7: Provides students with academically-focused descriptive feedback aligned to content-language objective(s) (High-Impact Instructional Moves
• I.8: Promotes students’ communication and collaboration utilizing appropriate digital and other resources (High-Impact Instructional Moves

Teachers are evaluated on a four-point rubric, where 1 is “Not Meeting” and 4 is “Distinguished.” The results of these evaluations for teachers in the district were compiled by the district and analyzed for each participating school. Researchers analyzed school-by-school factor runs ANOVA scale checks and possible correlation.

Figure 15. Sample questions from teacher survey

Figure 16. Sample questions from leader survey

Q#	Item	SPS Category
1	My teacher listens to me.	Supports Students
2	My teacher helps me understand my mistakes so that I can do better next time.	Facilitates Learning
3	My teacher makes sure that the class rules are clear.	High Expectations of Students
4	My teacher makes learning interesting.	Facilitates Learning
5	In my teacher's class, I have to work hard.	High Expectations of Students
6	Q06: My teacher explains what we are learning and why.	Facilitates Learning
7	My teacher ignores me (reverse-coded).	Supports Students
8	My teacher wants me to think about things I learn and not just memorize them.	Facilitates Learning
9	My teacher encourages me to share my ideas.	Facilitates Learning
10	My teacher makes sure that we all treat each other with respect.	High Expectations of Students
11	My teacher helps me learn new things.	Facilitates Learning
12	My teacher uses examples in class that I understand.	Facilitates Learning
13	I like the way my teacher treats me.	Supports Students
14	In my teacher's class, we learn to correct our mistakes.	Facilitates Learning
15	My teacher hurts my feelings (not used in scoring).	Filtering Use Only (not used in scoring)
16	My teacher checks to make sure I understand.	Facilitates Learning
17	In my teacher's class, I have to think hard about the work I do.	High Expectations of Students
18	My teacher believes in me.	Supports Students
19	My teacher makes sure that students do what they're supposed to be doing.	High Expectations of Students
20	My teacher only accepts my best effort.	High Expectations of Students
21	My teacher is good at explaining things that are hard to understand.	Facilitates Learning
22	I get bored in my teacher’s class (not used in scoring).	Filtering Use Only (not used in scoring)
23	My teacher explains things in different ways.	Facilitates Learning
24	My teacher makes sure that students in this class behave well.	High Expectations of Students
25	In my teacher's class, I have to explain my answers.	Facilitates Learning
26	My teacher is nice to me when I need help.	Supports Students
27	My teacher makes sure I do my best in school.	High Expectations of Students
28	The rules in my teacher's class are fair.	Supports Students
29	My teacher knows when the class does not understand.	Facilitates Learning
30	My teacher cares about me.	Supports Students

Table 1. Questions from student perception survey with coding categories

Decisions about Qualitative Coding

Groups of researchers worked with C-PEER in order to code archival documents for specified constructs, including: (a) STEM equity; (b) Teacher’s use of available time (TAUT); (c) English Language Learning (ELL) supports; and (d) Professional learning structures with coaching for Culturally Relevant Pedagogical (CRP). Taking guidance from Speer and Basurto’s (2012) calibration of qualitative data as sets for qualitative comparative analysis (QCA), researchers compared elementary schools systematically while trying to “give justice to within-case complexity” (Rihoux and Ragin, 2009; Speer, 2012, p. 156). Our research team decided not to perform a Qualitative Code Analysis, though the approach (thinking about how to define conditions related to outcomes) was an important part of our analysis framework. I contributed to the types of documents that I wanted accessed, and designed some codes based on expectations from current literature. I used those initial codes with my co-researchers and then refined them, continually performing multiple check-ins and corrections to be sure that I had inter-coder agreement. This was informally arrived at through code trials, discussions, and revisions of disagreements. This reflexive method of developing codes across coders allowed me to plan, code, monitor, and adjust throughout our coding sessions.

Data Collection Tools

We recruited seven urban elementary schools in an urban Colorado school district to participate in this study (N=7). Within these schools, we surveyed 186 teachers and educational leaders (n=105) for our sample subset. Schools were chosen using the Colorado Department of Education’s School Performance Framework. The seven participating schools ranked as follows: three “Performance Level” schools, two “Improvement Level” schools, one “Priority Improvement Level” school, and one “Turnaround Level” school. Using a range of elementary schools (multi-site) allowed researchers to analyze STEM foundational thinking and instructional activities as a comparative case study. Results from student perception surveys, leader and teacher surveys (quantitative data) were analyzed against students achievement and school academic growth data from school’s Unified Improvement Plans (UIP) and relevant trend (qualitative data), and archival structure data (e.g.: school schedules and team and committee workflows). These include observational data from classroom visits and curriculum and course schedule archives. In collaboration with C-PEER, researchers followed a short-cycle, iterative approach to research, working in collaborative learning teams. C-PEER and doctoral candidates formed research teams in order to co-design and create survey items.

Using Qualtrics Survey software, we administered the effective learning communities’ teacher survey by sending links out to building principals, to disseminate to their staff. This allowed for the greatest number of teachers to participate while remaining anonymous. Teachers were randomly assigned to take one of two versions of Qualtrics survey. This had the simultaneous effect of increasing the total number of items surveyed and keeping the survey short. We wanted teachers to stay engaged throughout the survey. The questions on each survey were similar in context (e.g.: STEM), but random in question order. For example, we grouped survey questions into nine areas: (a) lesson preparation; (b) staff collaboration; (c) student use of feedback and reflection on learning; (d) academic work relevant to students; (e) teacher access to instructional resources; (f) teachers engaging students in problem solving; (g) students participating in problem-solving activities; (h) teacher engagement with students’ families; (i) students showcasing mastery of academic content. This design supported our collection of reliable and valid data.

Quantitative Data Analysis

The teacher response rates for the Effective Learning Teacher Survey are between 37% and 100%. With regards to STEM-foundational thinking, teachers at each elementary school answered as follows: Annie Easley: 84%, Benjamin Banneker: 100%, Richard Spikes: 39%, Aprille Ericsson: 81%, Mae Jemison: 50%, Shirley Jackson: 37%, and Elijah McCoy: 50%.

Methodology (Mixed Methods Comparative Cases)

In collaboration with the Center for Practice Engaged Education Research (C-PEER), I analyzed available and relevant trend data for participating schools and school districts, archival data (e.g.: school schedules and team and committee workflows), data from common teacher and leader survey developed for the project (including asking teachers about 21st century teaching methods identified from the extant literature), and extant data from the schools’ district about students’ perceptions of school and evaluation data about teachers. We focused on understanding “effective learning community” systems through the lens of STEM-foundational thinking. Effective school learning communities, both inside the classroom, among teachers, and in relationship to school leadership work together to support or hinder a STEM mindset in students, especially students of color. We collected data to help schools understand how school structures and resources (e.g.: time, curriculum/instructional programs, equity of access, procedures), climate (e.g.: trust, leadership systems. STEM culture in buildings), and personal aspects (e.g.: teachers’ efficacy, student persistence and motivation) intersect. While each of these has been the subject of research in focused, disconnected studies, our collaborative approach with C-PEER brought data from each element of the system together for a comprehensive look at the interacting factors for improving access and opportunity to STEM curricula.

For our triangulation research design we use a mixed-methods, multi-site, comparative case study, using both quantitative and qualitative processes, in order to measure STEM foundational thinking

and instructional activities at the elementary school level. In some instances, statistical conclusions were limited by small sample size, but we received a very high response rate from school teachers and leaders. Where survey data did not lend themselves to standard quantitative analysis, they could still be considered as qualitative findings. Specifically, I am answering: (a) What elementary school structures support students in STEM curricular areas? (b) Do these supports differ for sub-groups of students, i.e. students of color, students in poverty, and English language learners? (c) What are the components of elementary STEM opportunities to learn that foster interest, participation, and academic success in STEM content areas, especially for marginalized students of color? The modified cultural historical activity theory (CHAT) framework, which undergirds this study allows us as researchers to triangulate our data points and understand what STEM-oriented activities (object) and goal-directed actions lead to STEM foundational thinking. Activity systems analysis is a method that helps to capture multi-mediational processes in human activity (Engeström, 1987, 1999). For example, in researching school structures at the elementary school level that give STEM access and opportunity to students of color, we need to capture information that will inform us about teachers’ mediational processes. 


A triangulation design is the best choice of methodology in order to ascertain what practices are in place in effective learning organizations for recruiting and engaging students of color in STEM curricula. The scope of prior research focuses on separate lines of inquiry (e.g.: STEM perspectives, STEM frameworks, Critical Race Theory, Culturally Responsive Education). By using a triangulation, mixed-methods comparative case study design, we were able to modify an established framework (CHAT), collect several quantitative and qualitative data points, and analyze elementary schools for STEM foundational thinking. For example, something in the rules/policies corner conflicts with something in the students’ ability to take on the role of a collaborative peer with others in their classroom. Researchers chose participating schools in an urban public school district based on a range of academic performance. Researchers used the Colorado Department of Education (CDE) School Performance Framework (SPF) to identify elementary schools based on student achievement and student growth in the 2015-2016 academic school year (ASY).

Planned Study Design

Our proposed research design was to use a mixed-methods, multi-site, comparative case study, using both quantitative and qualitative processes. Examining both quantitative and qualitative methods allowed for a more complete analysis of the research questions and findings (Tashakkori & Teddlie, 1998), as well as provide a broader basis for generalization of results (Simons, 1996). For example, this study intended to answer: (a) What elementary school structures support students in STEM curricular areas? (b) Do these supports differ for subgroups of students, i.e. students of color, students in poverty, and English language learners? (c) What are the components of elementary STEM opportunities to learn that foster interest, participation, and academic success in STEM content areas, especially for marginalized students of color? Our mixed-methods approach was guided by these research questions, and “ultimately reflect[ed] a value of both subjective and objective [STEM] knowledge” (Johnson & Onwuegbuzi, 2004; Tashakkori & Teddlie, 2003). We planned to use data sources centered on (a) recruiting schools to develop a focused priority research agenda; (b) conducting trend analyses of participating schools; and (c) a collaborative analysis of the research questions and recommendations for further action. It is important to note that mixed methods studies, such as this one, are strengthened when research teams are comprised of individuals from a variety of disciplines (Simons, 1996). This allows researchers to engage in a “mixed methods way of thinking” while discussing “different ways of seeing, interpreting, and knowing” about the data (Sammons, 2010, p. xi). Our C-PEER research team includes doctoral candidates from various K12 backgrounds including expeditionary learning, professional development, distributive leadership, and coaching teams. 

          Planned instrumentation. The C-PEER team planned to use instruments organized with specific constructs. For example, teacher surveys will measure the shared values and vision of teaches, as well as support conditions and relationships within the building. We also intended to collect information from both teachers and administration using the (Teaching, Empowering, Leading, and Learning) TELL Colorado surveys. These anonymous instruments would allow researchers to assess teaching conditions within school buildings, as well as throughout participating districts. Since these surveys would be designed to support school and district improvement planning, as well as inform policy decisions, our hope is that they would be extremely reliable and valid measures. It is our intention to design this study that will yield high-quality evidence for educators and school districts.

Chapter III. Mixed Method Comparative Case Study Design

Presently, there is no established agreement on the proper methodology of integrating STEM in elementary schools. However, I felt that interdisciplinary STEM education will be most successful at the elementary level, due to the fact that students spend most of their academic day with the same teacher in all content areas. Research indicates that traditionally underserved, minoirtized population of students engage in problem-based learning activities (e.g.: STEM-foundational thinking, instructional activities and assessments) will exhibit an increased performance in their overall academic achievement, critical thinking skills, and cooperative learning strategies (Cole, 1995; Tharp, R.G., Doherty, R. W., Echevarria, J., Estrada, p., Goldenberg, C., Hillberg, R.S., & Saunders, W.M., 2004). Therefore, we invited seven urban elementary schools from a large urban district to

participate in research designed to understand the school systems that may support STEM-foundational thinking and activities. We focused on the elementary school level because it is in these early academic years where students find their interests in STEM either helped or hindered. Our research analyzed school performance and process data (both quantitative and qualitative) and each school received a report designed to help them incorporate the findings into school improvement efforts. Seven elementary schools agreed to participate. Participation with this project intended to simultaneously help individual schools learn with and from other sites engaged in similar work. Led by the Center for Practice Engaged Education Research (C-PEER) at the CU Denver School of Education and Human Development, this technical assistance brought research expertise to support school improvement efforts. It helped fill gaps in local staff time, bridge challenges accessing performance and process data, and provide access to additional resources and learning from other school sites.

Conceptual Cultural Historical Activity Theory (CHAT) Framework

We used a modified activity theory framework, originating from Russian scholars (Hakkarainen, 2004), but made popular in North America by Scandinavian theorist Yrjo Engestrom (1987). Specifically, Engestrom combined both theory and practice, developing a formal methodology in research for mapping complex human interactions from qualitative datasets. In its original form, Engestrom included subject, took, object, rules, community, distribution of labor, and outcomes as shown in Fig. 1. 

Figure 1: Activity system adapted from Engestrom (1987)

As explained by Yamagata-Lynch and Smaldino (2007) “subjects are participants of the activity and tools are the resources that subjects use to obtain the object of goal” (p. 366). They continue saying, “rules can be informal or formal regulations that subjects need to follow while engaging in the activity. The community is the group that subjects belong to and the division of labor is the shared responsibilities determined by the community (Yamagata-Lynch & Smaldino, 2007, p. 366). Engestrom (1987) developed this framework to “explore and document the sources of tensions in human or collective activities” (Yamagata-Lynch and Smaldino 2007, p. 366). He wanted to help participants identify tensions in their practices and develop strategies to overcome them, allowing a collective action as opposed to individual efforts (Cole & Engestrom, 1993; Engestrom, 1987, 1993). Both individual and group activities are linked by their social context. Using applications of Engestrom’s activity system framework, along with more recent use from Yamagata-Lynch and Smaldino (2007), not only gives us a descriptive research tool in qualitative analysis, but will also help us research the complex social construction of educational organizations.

For use in evaluating and improving K-12 school and university partnerships, Yamagata-Lynch and Smaldino (2007) went through the methodological process of modifying the Engesstrom’s (1987) original activity systems analysis model. They developed specific language to represent the various components of the model (i.e., subject, tool, object, rules, community, and division of labor) so that teacher participants would be able to understand the framework. By adding questions related to each component (Fig. 2), participants did not need a theoretical background in the accompanying activity systems literature.

Figure 2: Modified activity system from Yamaga-Lynch and Smaldino (2007). Reprinted with permission of Elsevier. Yamagata-Lynch, L.C., & Smaldino, S. (2007). Using Activity Theory to Evaluate and Improve K12 School and University Partnerships.

For our overarching “Schools as effective learning organizations” collaborative learning community research study, we use the modified activity system model from Yamagata-lynch and Smaldino (2007) as a theory of action framework. The instruments shown in Figs. 3-4 engage both student learners and teachers as learners in order to achieve specific objectives.

Figure 3: Activity Analysis Framework: Student Learners C-PEER (2015)

Student Learners

The objective for our student learners is the co-creation of learning experiences with teachers, schools, and the students’ communities.

• Tools. In staying consistent with Yamagata-Lynch and Smaldino’s (2007) framework for an exploratory study of professional learning, we look at the resources that our student learners use in order to obtain the objective or goal. By asking What resources are currently available? and What resources do you need? we want student learners to help analyze what makes them successful as a learner. These include curriculum and instructional materials as well as classroom activities. We also look at home study supports such as available technology. We ask students survey questions about academic language and feedback used in the classroom, including self-assessments. Finally, an important tool we analyze is students’ self-regulation capacity. It is important for us to understand how students direct their own learning in school. Although there is no simple definition of the construct for self-regulation (Boekaerts & Corno, 2005), we hope this framework will help us as researchers to gain a detailed understanding of the cognitive and affective processes that underlie [the] actions that students initiate to regulate their motivation and learning in the classroom” (Boekaerts & Corno, 2005, p. 201). 
• Rules. The rules defined by the activity analysis framework are both the formal and informal norms, policies, and processes that make up an educational environment. For example, classroom and school norms, discipline-related expectations, homework policies, and student academic placement processes are all polices that student learners must navigate in order to be successful in school. 
• Community. The student learners’ community include the other students in the classroom, the teacher or any other supporting adults in the classroom, other students in the school, and parents or guardians engagement within the building. 
• Participation Structures. Whereas Yamagata-Lynch and Smaldino’s (2007) framework describes a division of labor, asking such questions as What specific responsibilities do you have to meet your goal? and What other responsibilities do you share with your colleagues to meet your goal?, we label this as participation structures due to the organization structure of schools. We are analyzing the organization of the learning activities within the school (e.g.: individual versus group projects and heterogeneous versus homogenous student grouping). We look at the support systems in place, such as teacher’s assistants and special education. Finally, we want to know what sorts of technology structures are in place within the classrooms. 
• Outcome and Indicators. Our intended objective for student learners is the co-creation of their learning experience with teachers, school, and the student’s community. Specifically, we are looking for students’ access to learning tools, cognitive, behavioral, and emotional engagement, and their abilities to gain appropriate knowledge and skills for their own defined success in school. Our data sets include performance trends, attendance and discipline data, and students’ perceptions of their school’s climate and the available social and emotional supports for students. 

Figure 4: Activity Analysis Framework: Teachers as learners C-PEER (2015)

Teachers as Learners

The objective for our teacher colleagues groups aggregated at the individual school level is the co-creation of learning experiences for their students as well as the continual improvement of skills, knowledge, and a mindset for effective teaching. Similar to the student learners, teacher participants are analyzing resources that they use in order to obtain the stated objective.

• Tools. Similar to the tools described in the activity analysis framework for student learners, teachers as learners need to ask the same questions: What resources are currently available? and What resources do you need? For teachers, their tools include, curriculum and instructional materials, timely access to student progress data, protocols and time for collaborative consideration of student work, response to intervention (RtI) processes, feedback from teacher evaluations, and options for professional learning. 
• Rules. Rules can be informal or formal regulations that teachers need to follow while teaching. These include instructional expectations, school norms as well as classroom norms, teacher resources for professional learning, collaboration among colleagues, and coaching, teacher evaluation systems, supports for innovations, and school requirements regarding professional learning communities (PLC’s). Our teacher surveys and focus group interviews will help us analyze schools as effective learning organizations. 
• Community. A teacher’s community includes many of the same supports for students (e.g.: students and other adults in the classroom); however, teachers can also rely on other adults in the school. For example, teacher leaders, colleagues, coaches, administrators, and evaluators. A teacher may also have access to external facilitators or trainers, specifically related to professional learning opportunities. 
• Participation Structures. We use the same questions Yamagata-Lynch and Smaldino (2007) ask for their framework, when analyzing teachers. Participation structures for teachers include time for planning and instruction, professional learning opportunities (whether required or by choice), and support systems (e.g.: coaching, mentoring, and access to teacher assistants and special education support). We want to analyze what structures are in place for peer learning and planning, supervisor/administrator-teacher interactions, and possible co-teaching. 
• Outcome and Indicators. The objective for teachers as learners is the same as for students; however we are adding a continual need for improvement of knowledge, skills and mindset for effective teaching. Teachers’ objective to co-create a learning experience for their students can be measured through student engagement, modifications in pedagogical practice, and teachers’ active participation in school-wide improvement and innovation. Measuring student engagement is a dynamic process that has limited theoretical research (Handelsman, Briggs, Sullivan, & Towler, 2005). There are many tools to measure engagement (Marzano & Pickering, 2011). Our outcome indicators include data-driven reflection.