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The Effects of Teachers’ Social and Human Capital on Urban Science Reform Initiatives: Considerations for Professional Development


by Susan A. Yoon, Jessica Koehler Yom , Zhitong Yang & Lei Liu - 2017

Background: Recent research investigating the conditions under which science teachers can successfully implement science education reforms suggests that focusing only on professional development to improve content knowledge and teaching skills—often referred to as human capital—may not be enough. Increasingly, possessing social capital, defined as capacities acquired through direct and indirect relationships in social networks, has become an important teaching characteristic to develop, however, more empirical research needs to be conducted.

Purpose: This article details our efforts to examine the relative influence of teachers’ social and human capital on instruction in the science classroom. The following research question guided our analysis: “What is the impact of teachers’ social and human capital on their classroom enactments, and what implications does this have for implementing science reform projects?”

Setting: This research was conducted in a large urban public school district in the northeast region of the United States. Teachers participated in professional development activities focused on learning about, constructing, and implementing nanoscale content through problem-based and inquiry-based units, integrated with technology applications such as computer simulations.

Population: The teacher group was comprised of 10 males and 11 females, eight of whom identified as African American and 13 as White. Teaching experience ranged from 1 to 33 years, with a mean of 11.18 years. Data were collected from 545 students in their classes, of whom 52.19% were African American and 65.03% received free or reduced-priced lunch. Students ranged in level between eighth and 12th grade in the subject areas of biology, chemistry, and physical science.

Research Design: The research design entailed a within group comparison assessing variables that quantified teacher’s social and human capital as discreet measures. They were then compared to survey outcomes collected from their students that indicated change in instructional enactments as they were related to the nanoscale units.

Data Collection and Analysis: A regression analysis was used in the study. Student surveys of perceptions of instructional enactments in two factors—cognitively-rich pedagogies and computer-related technology use–were used as the predicted variables. The social and human capital measures were established from application surveys and year-end interviews of teachers and used as predictor variables.

Results: With both predictors in the model, only social capital was found to be predictive of teachers’ successful implementation, indicating that social capital was a stronger predictor than human capital.

Conclusions: The study shows that focusing on the development of a teacher’s social capital may be an important feature of professional development activities alongside the development of human capital particularly in urban populations where access to resources is limited.



INTRODUCTION


Educational researchers and policy makers in the United States have recently made it a goal to improve urban science, technology, engineering, and mathematics (STEM) education (Ingersoll & May, 2012; National Research Council, 2011; PCAST, 2010). Toward that end, our research team conducted a 5-year professional development and implementation project, funded by a grant from the U.S. National Science Foundation, aimed at improving science teachers’ content knowledge, pedagogy, and curriculum development in a large urban school district. To develop our professional development and implementation framework, we used well-grounded educational studies based in research and practice (e.g., in professional development, Garet, Porter, Desimone, Birman, & Yoon, 2001) and worked with partners across industry and higher education in the sciences (National Science Board, 2010; National Research Council, 2011). Despite these efforts, not all teachers’ implementation achieved optimal changes in the classroom learning experiences of their students. This result led us to consider what might account for the differences in success, and what our next steps should be.


Recent research investigating the conditions under which science teachers can succeed suggests that focusing only on professional development to improve content knowledge and teaching skills—often referred to as human capital—may not be enough. Increasingly, possessing social capital, defined as capacities developed through direct and indirect relationships in social networks (Coleman, 1988), has become an important teacher characteristic in the adoption of new educational reforms. Research on teachers’ social capital suggests that social connections and relations can provide access to expertise (Frank, Zhao, & Borman, 2004; Penuel, Riel, Krause, & Frank, 2009); expand networks of trust and collegial interactions (Bryk & Schneider, 2002; Coburn & Russell, 2008); and identify and activate essential instructional resources (Spillane, Diamond, Walker, Halverson, & Jita, 2001).  


We also know that urban schools retain teachers at lower rates than nonurban schools, have difficulties attracting high-quality teachers (Ingersoll, 2012; Ingersoll & May, 2012; Maulucci, 2010; PCAST, 2010), and have limited funding for professional development (Blandford, 2012). These challenges are related not only to the lack of teaching resources but also to a lack of social support as studies have shown that social networking within schools has a positive influence on teacher performance and retention (Pogodzinski, Youngs, & Frank, 2013), which are correlated with higher student achievement (Ronfeldt, Loeb, & Wyckoff, 2012). Given these findings, we hypothesized that our emphasis on developing teachers’ human capital was not sufficient to assist our teachers in classroom implementation reform.


This article details our efforts to empirically test this hypothesis by examining the relative influence of teachers’ social and human capital on student classroom experiences. The following research question guided our analysis: “What is the impact of teachers’ social and human capital on their classroom enactments, and what implications does this have for implementing science reform projects?” In this study, we examine this question using data collected from 21 urban high school science teachers and the perceptions of instructional change from 545 students in their classrooms.


The rationale for this paper considers the research on the value of teacher social capital in conjunction with the challenges faced by urban schools such as underqualified teachers, high teacher turnover, and lack of funding for sufficient formal professional development. If social capital proves to be a significantly positive factor in implementing STEM education reform this could have several implications: (1) It could reshape the landscape of teacher professional development such that there would be less dependence upon funding for formal professional development sessions and tangible resources (traditional human capital approach), because teachers could be supported to informally share expertise and resources (emerging social capital approach); and (2) stronger emphasis on building social capital would result in higher teacher retention, improvement in teacher performance over time due to greater retention and ongoing built-in professional growth through collaboration, and in turn higher student achievement.


To date, despite recognizing the importance of social and human capital, teacher professional development is largely grounded in enhancing teachers’ human capital, and few researchers have investigated the relative influences of these forms of capital on teachers’ successful implementation of new curricula that are designed to improve student STEM learning in urban contexts. Given thesituated nature and variability of teachers, classrooms, and schools (Penuel, Fishman, Cheng, & Sabelli, 2011), STEM reform projects often require larger scale efforts (e.g., beyond single cases) to establish any relationship between a given reform and a generalized population. This, coupled with the relatively short federal funding cycles of typically 3 to 5 years for STEM reform projects, requires research teams to establish delimited goals to increase chances that implementation activities will be successful over the immediate term. Furthermore, because designed projects do not uniformly play out in the real world as envisioned by program developers (Lee, Penfield, & Maerten-Rivera, 2009), it would be helpful to understand which short-term and long-term steps to take to improve project efforts. In other words, investigating both teachers’ social and human capital characteristics during the intervention may inform decision making about necessary adjustments in the next iteration (Cobb, Confrey, Lehrer, & Schauble, 2003; Hargreaves & Fullan, 2012; Penuel & Fishman, 2012). When considering how to revise project activities, a stronger influence of social or human capital on project outcomes would potentially require different revision priorities and strategies.


For the analysis, we investigated various measures of social and human capital derived from existing literature. We operationalized these measures using codes that represent more or less social and human capital as possessed by teachers, and we used the measures to evaluate responses from teacher surveys and interviews. We then compared the teachers’ scores with their students’ responses on surveys that measured classroom experiences in two major project-related factors: cognitively rich pedagogies and technology integration.


THEORETICAL AND PRACTICAL CONSIDERATIONS


We begin this section with a brief discussion of the central issues underpinning the need for urban STEM education reform. We continue with a review of current practices and educational research to address these issues, which framed the development of our program activities. Finally, we review the literature on social and human capital and outline the measures that we applied in our study in order to evaluate the relative influences of these two forms of capital on our selected project outcomes.


NEED FOR URBAN STEM EDUCATION REFORM


One focus of recent discussions on improving the quality of STEM education has been the large achievement gap for underrepresented minority (African American, Native American, and Hispanic) students and the unequal distribution of educational resources in U.S. urban schools (Ingersoll & May, 2012; Maulucci, 2010). Citing various sources, a National Academy of Sciences (NAS) report (2011) revealed that in 2006, while underrepresented minorities comprised 28.5% of the U.S. population, they represented just 9.1% of the college-educated science and engineering workforce. Similarly, in 2007 underrepresented minorities comprised 38.8% of K–12 public school enrollment, but their rates of attainment of postsecondary degrees in science and engineering fields were much lower—only 17.7% for undergraduate degrees, 14.6% for master’s degrees, and 5.4% for doctoral degrees. The NAS report recommended that K–12 efforts focus on vastly improving mathematics and science education for underrepresented minorities and on improving the preparedness of teachers in those subjects.


Attention has also been cast on where the greatest need exists for resource allocation to improve underrepresented minority participation. For example, a recent PISA report stated that in large U.S. urban cities, where a disproportionate number of underrepresented students live, students performed under the OECD average, whereas suburban students scored higher (OECD, 2011). A number of resource issues related to teaching have been offered to explain this disparity in performance. The report notes that the United States is one of only four countries in which socioeconomically advantaged schools have access to more teachers than less-advantaged schools. Issues related to teacher turnover and quality have also been discussed in the literature. A study by Ingersoll and colleagues revealed that almost half of public teacher turnover in math and science happens in just a quarter of public schools, and those schools are located in high-poverty, high-minority, and urban areas (Ingersoll, 2011; Ingersoll & May, 2012). They cite working conditions as the greatest determining factor of turnover rates; examples of such working conditions include fewer opportunities for teachers to grow and learn as professionals, and access to fewer resources. High teacher turnover rates in minority urban schools have important implications for student access to a quality education, including learning from highly qualified teachers. Thus, developing teaching capacities, improving teacher preparation, providing more and better professional development, and offering related teacher activities to increase teacher retention have been high on the list of recent STEM education reform edicts in the United States (Ingersoll, Merrill, & May, 2012; National Science Board, 2010; National Research Council, 2011; PCAST, 2010).  


REFORMS AND PRACTICES RELATED TO DEVELOPING TEACHERS’ CAPACITY


In terms of developing teachers’ capacity, policy has called for a host of reforms that address both content and pedagogical knowledge. For example, recruiting more people who majored in science and math into teaching at the K–12 level and training existing teachers on current developments in STEM have been discussed in several influential reports (National Academy of Sciences, 2007, 2010; National Science Board, 2010; PCAST, 2010). Pedagogical training is also an important determinant of teacher retention, which itself is a necessary antecedent to developing teacher quality. For instance, Ingersoll et al. (2012) found that beginning science teachers received inadequate pedagogical training, which was significantly associated with higher attrition rates. It is therefore crucial to enhance teachers’ teaching capacities to improve both teacher quality and retention.


Policy researchers have also recently considered which teacher professional development methods to use in order to develop STEM teaching capacities. A survey of a national sample of over a thousand math and science teachers about the features of professional development activities that had a positive influence on their classroom practices resulted in the naming of five core activities (Desimone, 2009; Garet et al., 2001). Teachers identified a focus on subject matter content; opportunities to engage in active learning; coherence with teachers’ knowledge, beliefs, and daily classroom and school experiences; activities that had more than 20 hours of contact time and were ongoing over a period of time; and collective participation and interaction with other colleagues. Each core feature has a number of empirical studies attesting to the validity of its influence on classroom practice; however, collective participation has most recently gained prominence as a goal and method for teacher learning and professional development. Forms of collective participation that have been addressed in the educational literature include communities of practice (Little, 2002; Penuel et al., 2009; Wenger, 2000), teacher communities (Grossman, Wineburg, & Woolworth, 2001; Little, 2003), and professional learning communities (Dufour, 2004; Stoll, Bolam, McMahon, Wallace, & Thomas, 2006; Vescio, Ross, & Adams, 2008). In all its variants, the aim of collective participation is for teachers to develop shared understanding, responsibility, and action through collaboration and collective learning. We see a focus on teachers’ social capital as following in the same vein as these efforts and hope to contribute to important conversations around how professional development activities can be shaped to establish collective participation.  Discussion of the possible role of teacher social capital is still emerging (Daly, Moolenaar, Boliva, & Burke, 2010; Pogodzinski et al., 2013; Qian, Youngs, & Frank, 2013; Sikma, 2014).


We next discuss teachers’ capacity for social and human capital as it relates to our project aims. We do this to provide a sense of the landscape that we wish to build on and the measures that we used in the study.


IMPORTANCE OF SOCIAL AND HUMAN CAPITAL IN EDUCATION


Bourdieu (1986) explains that capital can be understood as accumulated labor that exists as a capacity to “produce profits and to reproduce itself in identical or expanded form” (p. 241). Resources that proffer tools and knowledge to accomplish this labor can be understood as either personal or social capital (Lin, 1999). Personal capital, akin to human capital as defined in the introduction, is possessed by individuals and can be used to complete tasks independently. In organizations, the development of personal, or human, capital happens through changes in people’s own skills and capabilities that enable them to act in new ways (Coleman, 1988). Conversely, social capital is acquired through direct and indirect relationships, and so necessitates making connections in one’s social networks (Lin, 1999, 2001). Adler and Kwon (2002) note that in organizational studies, access to social capital has been shown to be an important factor in explaining actors’ relative success in finding jobs, influencing career advancement, exchanging innovative product ideas, and strengthening supplier relations.


The Role of Social Capital in Educational Research


Educational researchers have investigated the role of social capital in individual teacher and organizational performance with respect to reform-oriented projects. In the areas of writing and literacy, teachers’ instructional practices were positively influenced through collegial interactions with other teachers whose practices had also changed, as well as through extensive expert-novice interactions (Penuel et al., 2009; Penuel, Sun, Frank, & Gallagher, 2012). With respect to implementation of computer technology innovations, Frank et al. (2004) found that social capital measures of perceived social pressure and access to expertise had significant influences on teachers’ use of computers in their instruction. In the area of urban school leadership and elementary science instruction, Spillane et al. (2001) found that the knowledge and skills of the leader on their own were not sufficient for influencing change in interaction. Instead, for real change to happen social capital variables such as communication within the school and networking outside of the school also had to be present. From this brief review of recent studies, the evidence reveals that teachers’ access to social capital influences how reforms play out in practice. When considering measures of social capital that could be applied to our data, we found the four determinants of social capital from Coburn and Russell (2008) to be useful. These are tie quality, motivation to share information, depth of interaction, and access to expertise. These four determinants were derived from an extensive literature review of studies focused on teachers’ social networks and aspects of social relations that create the conditions that foster development of teachers’ social capital. This framing of social capital is particularly relevant to our study in that our focal population is teachers who we hypothesize can increase their ability to enact classroom-based reforms by engaging in professional interactions. We discuss these categories in more detail in the methodology section.


The Role of Human Capital in Educational Research

 

The current emphasis on international benchmarking and the rhetoric around the decline of the United States as a global leader in STEM research and innovation has forced a serious examination of current K–12 STEM instruction and the need to create a highly qualified teaching workforce with an emphasis on developing teachers’ human capital. We define human capital, briefly discussed in the introduction, as individual skills and capacities, such as teachers’ content and pedagogical content knowledge, that can be used to complete tasks (Lin, 1999, 2001; National Academy of Sciences, 2007, 2010; OECD, 2011; PCAST, 2010). Such arguments for the development of human capital have been levied for some time in sociological research (e.g., Coleman, 1988; Lin, 2001) and also in education (e.g., Spillane, Hallett, & Diamond, 2003).


With respect to how to measure human capital, at a basic level, Lin (1999) suggests that an actor’s education and experience should be considered. Furthermore, in a review of studies on teacher quality, Darling-Hammond and Youngs (2002) showed that several aspects of teachers’ human capital, including subject-matter knowledge, knowledge about teaching and learning, and teaching experience, had some relationship to student achievement. Pil and Leana (2009) measured math teachers’ human capital in terms of educational background, relevant teaching experience, and task-specific experiences, thus using criteria that could account for a teacher’s knowledge and skills that were applicable to math teaching at the given grade level. We adapted Pil and Leana’s framework to account for four features: educational background, years of teaching experience, experience with cognitively rich pedagogy, and experience with computer-related technology. The latter two features were included to account for relevant teaching and task-specific experiences related to our reform goals. We discuss these categories in more detail in the methodology section.


EVALUATING SOCIAL AND HUMAN CAPITAL WITH TEACHERS


Studies of social and human capital generally examine the impact of one or the other, but a few studies have looked at the relative importance of both forms of capital in achieving educational goals. Leana and Pil (2006) investigated the effects of internal social capital (connections between actors within an organization) and external social capital (connections between an organization and outside resources) on organizational performance in urban public schools. They measured teacher human capital using the variable of teacher experience and found a weak correlation between experience and instructional quality. Conversely, their results indicated that internal and external social capital were important determinants of student achievement in both reading and math and were significant predictors of instructional quality.


Daly, Moolenaar, Der-Martirosian, and Liou (2014) similarly measured teachers’ human and social capital to test the hypothesis that social capital would have a positive relationship with student achievement in reading comprehension. Their study found a relationship between teachers’ human capital as measured by number of years of teaching experience and number of years teaching in their school’s context, and student achievement. They also found that a teacher’s social capital was significantly related to student achievement above and beyond teacher’s human capital.


In a study with a similar context to ours in terms of influencing teacher practice through professional development, Penuel et al. (2012) found that teachers who had received help from other teachers who had changed their practice as a result of professional development also changed their practice. Elsewhere, in a study of a summer professional development workshop, we also explored human capital-related indicators of teachers’ abilities to work collaboratively on curriculum construction (Yoon, Liu, & Goh, 2010). We used the complex systems concept of adaptation to understand how and why some of our first cohort teacher groups were better able to adapt to the program’s curricular requirements. The results showed that when teachers with comparable levels of human capital were grouped together, the groups were more likely to have dynamics that resulted in convergent adaptive outcomes (e.g., shared meaning, distributed expertise). Finally, in another of our previous studies, we wrote about a construct we called practitioner-based social capital, which we defined as the resources, information, and support for effective STEM teaching that are available through a teacher’s social network (Baker-Doyle & Yoon, 2011). In that study, we were interested in the interactive relationship between teachers’ human and social capital, and the goal of building emic capacity within the community. We assessed teachers’ perceptions of their colleagues on two variables: their mentoring qualities (i.e., a form of trust) and their teaching experience characteristics (measured through number of years teaching, leadership position held, previous professional development experience, and understanding of subject-matter content). We found that teachers who were seen as having strong mentoring qualities were sought out more than teachers who had strong experience characteristics. We further found that teachers who had high experience characteristics were more isolated in the social network, rendering their potentially helpful experience ineffectual to other teachers.


This growing body of research, as well as the research reviewed above on developing teaching capacities, suggests that social and human capital influence the quality of practice and instruction. Our aim is to explore their relationship to, and influence on, our project outcomes. Given the finite resources for professional development in all schools, and the unique capacity challenges facing urban schools, it is prudent to understand the relationship between the two forms of capital and how they influence classroom practice in order to tailor efforts to achieve greater reform impacts especially with urban populations.


METHODOLOGY

PROJECT CONTEXT

This work is part of a comprehensive, large-scale project funded by the U.S. National Science Foundation under the program title Innovative Technology Experiences for Students and Teachers (ITEST). The ITEST program is designed to increase opportunities for populations in underserved schools to learn and apply information technology concepts and skills in the STEM content areas. Through our project on the topic of nanoscale science, we aimed to achieve the broader ITEST goals by developing a curriculum and instruction framework built on five component variables addressing content knowledge, pedagogical content knowledge, and workforce development. These variables are (a) real-world science and engineering applications; (b) educational technologies to build content knowledge; (c) information technologies for communication and dissemination; (d) cognitively rich pedagogical strategies (e.g., problem-based learning; PBL); and (e) investigations of STEM education and careers.


There were two parts to our project. The first part entailed a 3-week, 75-hour summer professional development workshop in which high school science teachers learned to construct and pilot PBL units in subject-specific teams based on the five component variables of the project framework. These curricular units were aligned with school district standards for high school science. The summer workshop was followed by the second part of the project—the school-year implementation of these units in teachers’ classrooms, which were supported by four follow-up Saturday workshops as well as in-class facilitation from research team members. Professional development activities were based on the known variables of high-quality professional development discussed earlier in this article (Desimone, 2009; Garet et al., 2001). Teachers participated in extensive content-knowledge development delivered by professors, post-docs, and doctoral students who worked at a large university center focused on cutting-edge nanoscale empirical research. Teachers’ hands-on training involved doing the same activities that their students would, constructing curriculum surrounding the activities, and then teaching summer school students. Teachers were asked to consult the school district’s planning and scheduling timeline to align the constructed curriculum with the daily demands in the classroom. Professional development contact time far exceeded the 20-hour minimum and was ongoing throughout the year. Finally, teachers were required to engage in collaboration when constructing and implementing their curricular units in the summer. They worked together in teams of two or three developing common lessons, activities and assessments to teach an agreed upon unit within their selected science content area. Where possible, we recruited teachers from the same school and who worked on the curriculum together.


STUDY POPULATION CHARACTERISTICS


This study is based on data collected in years 1 through 3 (2008–2011) of the 5-year project. Of the 47 total teacher participants, we collected a complete set of student and teacher data from 21 teachers in 12 high schools and five middle schools. The group was comprised of 10 males and 11 females, eight of whom identified as African American and 13 as White. Teaching experience ranged from 1 to 33 years, with a mean of 11.18 years. Within this range, nine teachers had little teaching experience (less than 5 years with an average of 1.4 years); three teachers had a fair amount of teaching experience (between 6 and 10 years, with an average of 8 years); and 10 teachers had a lot of teaching experience (11 years or more, with an average of 20.9 years). Surveys were collected from 545 students in their classes, of whom 52.19% were African American and 65.03% received free or reduced-priced lunch. Students ranged in level between eighth and 12th grade in the subject areas of biology, chemistry, and physical science.


SOCIAL AND HUMAN CAPITAL MEASURES


We gathered information about teachers’ social capital from interviews conducted after they completed the project. Such self-report methods of social interaction are commonly used in social capital and social network studies (e.g., Daly et al., 2014) to gather detailed information about who teachers interacted with during their daily school activities outside of what is more objectively observable in PD workshop situations. The following questions required teachers to discuss with whom they shared information and maintained communication over the course of the school year.

What kind of communication have you had with people from the summer PD? How often? About what? How did this communication help you implement your unit and grow professionally?


Have you talked to other people in your school about nanoscale science, technology, or other aspects of the project? What did you talk to them about in relation to the project? How often?


Did you talk to teachers in other schools? What did you talk about in relation to the project? How often?


Transcripts of their interview responses were qualitatively mined and coded for the social capital categories modified from Coburn and Russell (2008). The four categories were (discussed in more detail in Table 1):


Tie Quality: How many people teachers talked to in relation to the project implementation (i.e., tie span) and the frequency of these interactions (i.e., tie strength) (Leana & Pil, 2006).


Motivation to Share: How willing teachers were to share information. In terms of capital and accessing resources, teachers may be motivated to share information about the project with the tacit expectation that they receive reciprocal information or resources (Adler & Kwon, 2002).


Depth of Interaction: The content of interactions that were more or less related to the project activities or goals. These interactions could range from more administrative exchanges to deeper conversations about learning goals (Coburn & Russell, 2008).


Access to Expertise: The competencies and resources available in teachers’ network connections (Adler & Kwon, 2002; Coburn & Russell, 2008) as well as teachers’ knowledge of these competences and resources and their ability to access to them.


The interview questions were constructed to yield responses in the four social capital categories within the context of our study. There isn’t a one-to-one direct mapping of interview questions onto the categories, as we wanted to enable teachers to discuss their experiences, which were authentically anchored in the study and did not want to lead them to respond in particular ways. We mined the whole transcript to code for the social capital categories. It is important to note that in some cases, we made inferences about how close teachers were to the people that they mentioned in their interviews based on the entire year’s project experiences and observations with them. For example, in the Medium code for the category Tie Quality in Table 1, Jake and John worked in the same school and in classrooms beside each other. We know that they corresponded and shared ideas a lot based on informal conversations with them. This was coded as a close tie, however, we know that Jake’s correspondence with their third partner not as close or often, therefore Jake was coded as Medium in this category. Under the same category in the High code, the excerpt shown there mentioned that Sharon had communications with her colleague Stan. In this case, we know that Sharon and Stan signed up to participate in the project together and worked at the same school for many years. Sharon and Stan sat with each other during the summer and Saturday professional development workshops exchanging locally specific information about their school. It was clear that they were close colleagues from their interactions therefore their tie strength was high. Sharon also mentioned talking to Genny, Nick, and Sarah who were participants in the project. We know that Sharon actually reached out to several other participants about the project, therefore her tie quality overall was coded as high.


To determine teachers’ levels of human capital, we used initial surveys that teachers completed before participating in the project. The surveys asked questions related to their education, how long they had taught, and the kinds of experiences they had with two core project goals (i.e., using cognitively rich pedagogies and computer technologies in the classroom). Following Pil and Leana (2009), we used these four categories of human capital (discussed in more detail in Table 2):


Education: Teachers’ undergraduate majors in relation to the project’s content domains, e.g., biology, physics, and chemistry.


Teaching Experience: The number of years teachers taught.


Experience with Cognitively Rich Pedagogy: The extent to which teachers used or had been previously trained to use cognitively rich pedagogy, such as student-centered or inquiry-based learning.


Experience with Computer-Related Technologies: The extent to teachers used or had been trained to use computer related technology in the classroom.


To conduct the coding, two researchers on the team independently mined all 21 interview transcripts for instances that illustrated each category using the categorization schemes found in Tables 1 and 2. Relevant instances were coded and teachers were assigned a low (1), medium (2), or high (3) in each of the categories based on their responses to the three interview questions and initial surveys. The following excerpts represent responses from one of the study participants that received a high (3) code for the social capital category of Motivation to Share:


My principal . . . supports whatever I'm doing. I am totally ready for her to come in and that's why I wanted you there to see.


I could have had parents going crazy because [students] had this project due and they didn't have the stuff at home to do it. Over Christmas vacation I took a computer to a kid's house, a laptop.


I have been waiting for some parent to complain and the only thing I got back was parents appreciating what the kids are doing. In fact parents have called me to apologize because their kid couldn't incorporate whatever it was that night because the brother or sister had to do their homework because they were monopolizing the computer.


I have already talked to one of my teacher friends who I think would be really good [in this project]. She's a true leader in the community. I think she'd be a perfect person to do this.


The category of Motivation to Share builds social capital in that teachers actively or intend to share information with others in order to gain access to resources that will help to accomplish a task. In this set of responses, the teacher talked about the intention to show her principal the work she had been doing with her students on the project to continue to gain support. She informed parents about the project and assisted under resourced students to complete the project on time. This teacher also started to enlist her colleague to participate in the next professional development workshop who was a leader in the community. An assumption could be made that bringing in leaders to work on the project would also help this teacher to be successful.  In the same category, the following response from a teacher was coded a low (1):


Ms. Apple is our chemistry teacher and she has already been through a thousand PDs and Mr. Marchant unfortunately doesn't really do professional development. Our science department is only three people.


For this teacher, motivation to share anything about the project was low based on his perception of his colleagues and their potential interest in participating.


A comparison of initial codes revealed 80% agreement between the two researchers. All discrepant codes were discussed and a final code was agreed upon. We then went through a process of obtaining interrater reliability for the coding scheme, which was conducted on 50% of the data (11 interviews) with a researcher external to the project. Cronbach’s alpha reliability scores showed good to excellent agreement between the external researcher and the codes initially assigned: Tie Quality = .933; Motivation to Share = .758; Depth of Interaction = .877; and Access to Expertise = .920. We discussed discrepant codes in each of the categories and agreed on a single code. The four codes for social capital were summed and averaged to produce a single code for the analysis that follows. The same happened with the human capital categories and codes.


Table 1. Categories of Social Capital Coding Scheme

Category of Social Capital: Definitions

Low (1)

Medium (2)


High (3)

Tie Quality: Includes tie span and strength

Tie Span: Includes all those who are in the project network who could potentially help with the implementation. This includes members of the research team and other teacher participants.

Tie Strength: Measures the frequency of interaction and the degree to which the teacher felt close to the tie. Was it someone they worked closely with in the summer who helped plan the curriculum? Was the liaison a facilitator who worked closely with them to help implement the project?

Did not make connections with other members of project about project-related activities


I don’t really have contact with them at all. I tried to reach out.

(Ron, Cohort 1)

Made connections with one or two members of the project about project-related activities either often or with someone to whom they felt a close tie


John is right next door. We’ve been talking a lot about it. I’ve emailed with our 3rd partner.

(Jake, Cohort 1)

Made connections with several members of the project about project-related activities often and with someone to whom they felt a close tie


I did have communications with my colleague, Stan.

Yeah, I also had communications with the other chemistry teacher.

Genny and Nick, I have met at the Saturday workshops”

Sarah…We talked about the implementation on the phone. Twice.

(Sharon, Cohort 2)


Motivation to Share: The willingness of teachers to share information, and their rationales for doing so. Do participants tend to share information with those who they can potentially get return favors from or garner support from in future implementations? Information shared with a principal might generate such returns, whereas sharing information with a student teacher probably would not. Sharing information with other members of the teaching staff could potentially build momentum in the school so that would be good motivation to share.


Did not share information with anyone who would be able to offer future support or expand their social capital


No I didn’t really talk to anyone about the project.”

(Tom, Cohort 3)


Shared information with at most a few people who would be able to offer future support or expand their social capital. The interactions are reported to be informal and rather insubstantial.


Yeah, I have talked to a few people. Like I said, one of my friends teaches here so he knows about it.

(Zane, Cohort 1)


Shared information with at least several people who would be able to offer future support or expand their social capital. The interaction involves significant outreach and/or planning on the part of the participating teacher.


I talked to a group of teachers we meet at [the university]. I told them I was doing this unit and I explained all the 5 components of the unit. Also I have a teacher friend, at a school in [West City]. She wanted to implement the unit so I shared it with her. I shared with a group of around 6 teachers [at Master Teachers Initiative meeting].

(Sharon, Cohort 2)



Table 1. Categories of Social Capital Coding Scheme - Continued


Category of Social Capital: Definitions


Low (1)


Medium (2)


High (3)

Depth of Interaction: The quality and level of interactions between teachers. Are the interactions about aspects that are essential to implementation, such as how to set up technology in the classroom or are they simply about how it’s going. Is the interaction highly related to the project activities or more general teaching questions?

Interactions are not related or are minimally related to the project implementation


“Interviewer: How often do you talk to Kam?

A: Couple times a week

Interviewer: Is it related to Nano?

A: Not usually”

(Aubrey, Cohort 3)

Interactions are related to the project but are not about the details of implementation


I had some other people you know . . . folks that I knew from other contexts. So we had a kind of, you know, that same reference point to collaborate with each other.

(Bart, Cohort 2)

Interactions are related to the project and are about details related to implementation


With John, I talk about everything, what works, what changes I made in the curriculum. With Mark, it was more technology questions – how to do Google Groups.”

(Jake, Cohort 1)

Access to Expertise: Whether teachers had access to others who had expertise in areas related to the project. Did participants access experts who would be able to help them in any of the project goals and did they get that help? For example, did they access the technology person at the school to help them with the project? Did they access participants who they perceived to have more knowledge than them on any of the five components? Did they access their principal to help them get resources for the project?

Either did not access people who had expertise in implementing any of the components of the project framework, or attempted to access these individuals but received no support in implementation


There are not natural connections, and I’m the only one in my school doing it.

(Ron, Cohort 1)

Accessed at least one person who had expertise in implementing any of the components of the project framework and received adequate support in implementation


We do have a tech person that comes in and helps with like networks and things like that.

(Sheila, Cohort 3)

Accessed more than one person who had expertise in implementing any of the components of the project framework and received adequate support in implementation


I actually go up to the third floor and use the lab up there because there’s a lab assistant and I’ll ask her to set up so many stations.

So I had one of my techies come over and said why isn’t this working . . . he went home and came back and it was working.”

My cousin is a cardiologist and I was going to ask him if they have any new [nano] materials that they’re using for [heart] stents.”

(Denise, Cohort 1)


Table 2. Categories of Human Capital Coding Scheme


Categories of Human Capital: Definitions


Low (1)


Medium (2)


High (3)

Education: Undergraduate degree related to the core content areas of the project (biology, physics, chemistry)

Undergraduate degree is not in a science content area

Undergraduate degree is in a related science content area (e.g., microbiology)

Undergraduate degree is in one of the core science content areas

Experience: Number of years teaching

5 or fewer years

Between 6 and 10 years

11 or more years

Experience: Cognitively rich pedagogy: Experience in student-centered and inquiry-based learning.

No evidence or mention of classroom use or professional development exposure

Basic mention of student-led research projects or accounting for student learning and/or training in student-centered learning


"We have had several [Professional Development Workshops] here at our school dealing with students accessing their own data and following through with science research in a very broad sense."

(Patricia, Cohort 3)

Detailed description of student-centered or inquiry-based learning and/or extensive training in student-centered or inquiry-based learning


"I had the students do a Webquest on carbon monoxide. They were put into groups and assigned different task…and the goal was to use the guiding questions given to create a brochure warning of the dangers of carbon monoxide. In doing this activity, students used technology to make real-world connections, gain information and also create material."

(Genny, Cohort 2)


Experience: Computer-related technology: Experience in using or being trained in using computer technologies in the classroom.


No evidence or mention of classroom use or professional development exposure, or explicit discussion of the lack of experience


"I have not had a lot of experience because our school, rather our department, doesn't always support teachers with the additional things that are needed to use them [technology] in the classroom"

Rose (Cohort 2)


Description of use of/training in basic technological tools such as PowerPoint or Internet videos, etc.



"The technology that I have used in my classroom is rather basic…Often times my classes will have a web-based activity in which they are required to use laptops on the internet. I personally have sufficient knowledge of Microsoft applications."

(Zane, Cohort 1)


Elaborate use of or training in technology specifically to support student-centered science learning




"I use instructional technologies in the classroom all the time. I am one of the two “Classroom for the Future” teachers in the science department. I have an interactive whiteboard and access to a computer cart."

(Sheila, Cohort 3)


STUDENT CLASSROOM EXPERIENCES RELATED TO THE PROJECT IMPLEMENTATION


To understand the relationship between teachers’ social and human capital and their ability to implement the reform project in their classrooms, we needed to measure growth in relevant instructional variables. We based this measure on students’ perceptions of teachers’ instructional change (see Ferguson, 2012, for discussion about using student surveys as measures of teaching quality). Specifically, we asked students to complete pre- and postintervention surveys that measured frequencies of cognitively rich pedagogy and computer-related technology use by their teachers, ranging from “never” to “every day.” Questions related to cognitively rich pedagogies measured teachers’ ability to incorporate inquiry-based, student-centered, and collaborative instructional approaches. Questions related to computer-related technology measured teachers’ use of computers and related technologies in their instruction.


Table 3 describes the results of an exploratory factor analysis of the survey responses. The average score of all items under each of the two factors was calculated and used as the factor score. For each factor, a repeated-measures ANOVA was conducted to understand growth patterns in students’ classroom experiences. Those teachers whose students perceived significant negative growth were coded as having Negative Growth (1). Those teachers whose students perceived no significant negative or positive growth were coded as having No Growth (2). Teachers with significant positive growth, as perceived by their students, were coded as having Positive Growth (3). In the factor of cognitively rich pedagogy, about 24% of the teachers were perceived by students as having negative growth, 67% were perceived as having no growth, and 10% were perceived as having positive growth. In the factor of computer-related technology use, about 5% of the teachers were perceived as having negative growth, 71% were perceived as having no growth, and 24% were perceived as having positive growth (see Table 4 for descriptive statistics). This coding was used as the dependent variable in the logistic regression analysis that follows.


Table 3. Curriculum and Instruction Survey Factor Description

Loadings

Item Wording

 

Factor 1: Cognitively Rich Pedagogy (α  = .89)

0.47

The teacher shows us how to solve science problems.

0.44

We learn about scientific applications.

0.63

The teacher gives us opportunities to hypothesize, predict, test and modify hypotheses.

0.57

We collect data to test and modify hypothesis.

0.47

We relate scientific knowledge to explaining real world phenomena.

0.58

We work together in pairs or small groups to negotiate ideas.

0.61

The teacher demonstrates experiments.

0.59

We do experiments or practical investigations in class.

0.35

We evaluate each other's work.

0.66

The teacher gives us opportunities to find out answers on our own.

0.66

The teacher asks questions when we do experiments.

0.57

We discuss the pros and cons of problem solutions.

 

Factor 2: Computer-Related Technology Use (α = .83)


0.35

We use computer simulations, images, or animations to collect and analyze data to draw conclusions.

0.82

We use computer models to visualize scientific phenomena.

0.82

We use computer models to conduct experiments and produce evidence-based reasoning.

0.67

We search for scientific information on the Internet.

0.46

We discuss or share ideas with people (e.g., Google Groups, email, wikis, logs).

0.48

The teachers use computer technology (e.g., Google Groups, email, wikis, logs) to post messages to the class.


Table 4. Minimum, Maximum, Mean, and Standard Deviation of the Coding (N = 21)

 

Min

Max

Mean

Std.

Social capital score

1

3

1.95

.67

Human capital score

2

3

2.67

.48

Growth in TechUse

1

3

2.19

.51

Growth in CogRich

1

3

1.86

.57


DATA ANALYSIS


Given the ordinal nature of the outcome variables, (i.e., the dependent variables were coded as negative growth [1], no growth [2], and positive growth [3]), and our goal of finding out the relative importance of teachers’ human and social capital in predicting successful implementation, we conducted three ordinal logistic regression analyses (O’Connell, 2006; Tabachnick & Fidell, 2007). We first used human capital as the single predictor (Model 1). Next, we used social capital as the single predictor (Model 2). Finally, we used both human and social capital as predictors (Model 3). An alpha level of .05 was set for all significance tests in the study. In the models, the negative growth served as the first group to form the cumulative odds at the two ascending cutoffs (O’Connell, 2006)—that is, no-growth and positive-growth groups. Standard procedures were also applied to evaluate the goodness of model fit (Cohen, Cohen, West, & Aiken, 2003; Norusis, 2012).


RESULTS


Table 5 summarizes the model fit information. For both growth in use of cognitively rich pedagogy and growth in use of computer-related technology, Table 5 shows that both Model 2 (social capital) and Model 3 (both social and human capital) meet the model fit criteria as indicated by the nonsignificant Pearson test, and increase the predictive power with the predictor(s) in the model as indicated by the significant likelihood ratio test, χ2 (1, N = 21) = 4.807, p < .05; χ2 (2, N = 21) = 6.455, p < .05; χ2 (1, N = 21) = 8.155, p < .05; χ2 (2, N = 21) = 8.165, p < .05. In addition, there is a linear relationship across the two levels of growth—in other words, from negative growth to no growth and from no growth to positive growth—as indicated by the nonsignificant chi-square test of parallel lines, which suggests that one equation is valid. In plain English, this means that teachers’ frequency of use of cognitively rich pedagogy and computer-related technology, as measured by their students’ classroom experiences, increases with teachers’ possession of social capital in a linear fashion. Put even more simply, the more that a teacher possesses social capital, the better chance that the teacher would frequently use the targeted pedagogy and technology. Although Model 1 (human capital) also meets the model fit criteria, compared with the baseline model (i.e., a model with no predictor), adding human capital as a predictor in the model does not increase predictive power. In addition, for growth in use of computer-related technology, a linear relationship cannot be assumed across the two levels of growth, χ2 (1, N = 21) = 4.807, p < .05. In other words, human capital does not predict the frequency of teachers’ use of targeted pedagogy and technology in our study.


Table 6 summarizes the ordinal logistic regression parameters. As a single predictor in the ordinal logistic regression model, only social capital was found to be predictive of the frequency that teachers used targeted pedagogy and technology (β = 1.921, p < 0.05; β = 2.785, p < 0.05). That is, with a 1-unit increase in measure of teachers’ social capital, the chance of the teacher will increase the frequency by about 7 times, for using targeted pedagogy; and by 16 times, for using targeted technology.


When both human capital and social capital were included in the model, still only social capital was found to be predictive (β = 2.688, p < 0.05; β = 2.845, p < 0.05), which indicates that after controlling for the effect of human capital, social capital still predicts the frequency of a teacher using cognitively rich pedagogy and computer-related technology.


Furthermore, with both predictors in the model, only social capital was found to be predictive of teachers’ successful implementation, indicating that social capital was a stronger predictor than human capital. For each unit of increase in teachers’ social capital measure, the possibility of increasing frequency is about 15 times (i.e., eβ = 14.702) for cognitively rich pedagogy and about 17 times (i.e., eβ = 17.202) for computer-related technology use, when the human capital in the model is held constant.


Table 5. Summary of Model Fit of Ordinal Logistic Regression of Cognitively Rich Pedagogy and Computer-related Technology on Teachers’ Human and Social Capital (N = 21)

Overall model evaluation

χ2

df

p

Growth in Use of Cognitively Rich Pedagogy

Model 1

Likelihood ratio test

.002

1

.963

Pearson

.494

1

.482

Test of parallel lines

.477

1

.490

Model 2

Likelihood ratio test

4.807

1

.028

Pearson

18.245

15

.250

Test of parallel lines

0.635

1

.425

Model 3

Likelihood ratio test

6.455

2

.040

Pearson

31.077

22

.095

Test of parallel lines

1.552

2

.460

Growth in Use of Computer-related Technology

Model 1

Likelihood ratio test

1.919

1

.166

Pearson

3.428

1

.064

Test of parallel lines

4.087

1

.043

Model 2

Likelihood ratio test

8.155

1

.004

Pearson

10.539

15

.785

Test of parallel lines

0.394

1

.530

Model 3

Likelihood ratio test

8.165

2

.017

Pearson

13.746

22

.910

Test of parallel lines

3.252

2

.197


Table 6. Summary of Ordinal Logistic Regression of Cognitively Rich Pedagogy and Computer-related Technology on Teachers’ Human and Social Capital (N = 21)

Parameter

eβ

β

SE

df

Sig.

Growth in Use of Cognitively Rich Pedagogy

Model 1

Threshold

     

Between negative growth and no growth

 

-1.041

2.619

1

.691

Between no growth and positive growth

 

2.374

2.684

1

.376

Location

     

Human Capital

N/A

.046

.965

1

.962

Model 2

Threshold

     

Between negative growth and no growth

 

2.142

1.639

1

.191

Between no growth and positive growth

 

6.386

2.395

1

.008

Location

     

Social Capital

6.828

1.921

.967

1

.047

Model 3

Threshold

     

Between negative growth and no growth

 

-.820

2.811

1

.770

Between no growth and positive growth

 

3.721

3.092

1

.229

Location

     

Human Capital

 

-1.596

1.271

1

.210

Social Capital

14.702

2.688

1.219

1

.027

Growth in Use of Computer-related Technology

Model 1

Threshold

     

Between negative growth and no growth

 

0.797

3.148

1

.800

Between no growth and positive growth

 

5.360

3.573

1

.134

Location

     

Human Capital

N/A

1.539

1.250

1

.218

Model 2

Threshold

     

Between negative growth and no growth

 

1.289

1.939

1

.506

Between no growth and positive growth

 

6.897

2.684

1

.010

Location

     

Social Capital

16.200

2.785

1.203

1

.021

Model 3

Threshold

     

Between negative growth and no growth

 

1.007

3.418

1

.768

Between no growth and positive growth

 

6.591

3.908

1

.092

Location

     

Human Capital

 

-.149

1.448

1

.918

Social Capital

17.202

2.845

1.328

1

.032


DISCUSSION


Using student perceptions of change, we measured growth in implementation of project activities in two classroom practices that involved the use of cognitively rich pedagogies and computer-related technology. In our professional development intervention, which was based on best practices research, we sought to improve aspects of teacher quality that could be considered in the realm of human capital (e.g., teacher’s content and pedagogical knowledge). From the analysis of classroom implementation, we determined that a focus on human capital was not enough to achieve success. Although we did attempt to develop teachers’ social capital by, for instance, creating opportunities for teachers to develop a collaborative curriculum and requiring teachers to program student collaborative activities into their curriculum, they did not by and large collaborate with each other well. Furthermore, we did not specifically discuss with teachers the need to reach out to each other during the school year implementation or to reach out to colleagues in their schools to support their efforts.


We hypothesized retrospectively that this lack of social capital might have played a more important role in the success of the project than we had originally anticipated especially considering the urban population we worked with. Thus, we were interested in investigating whether teachers’ social and human capital were significant predictors of growth and, of the two, which was more predictive. A review of the teacher capital literature shows that both are important in developing teacher quality (Coburn & Russell, 2008; Daly et al., 2014; Hargreaves & Fullan, 2012; Penuel et al., 2009; Pil & Leana, 2009), and that stronger teacher social and human capital is more effective than human capital alone (Daly et al., 2010), however, research directly evaluating the relative importance of both in achieving educational goals is still emerging.


Interestingly, the results of our study showed that human capital was not a predictor at all when both human and social capital were included in the regression model. We recognize that there may have been other reasons for why human capital was not predictive such as the small sample size and the relative less variation among teachers’ human capital scores. With our population of teachers, a little less than half had fewer than 5 years of experience and about half had 11 years or more of experience. The newer teachers tended to have more experience using technology while the older teachers had more experience teaching, thus these measures may have balanced each other out. We adapted previously published categories of human capital measures (Pil & Leana, 2009) for this study. For future studies, we may need to include more or different categories derived, for example, from observations of actual instruction in classrooms to get a more accurate and nuanced picture of a teachers’ human capital especially when working with interventions that are focused on integrating technology.


However, whereas much emphasis has been placed on improving teachers’ human capital qualities including our own emphasis in the nanoscale intervention, our results provide important evidence for the potentially critical role of social capital, which has largely been ignored in the area of teacher training. As Coleman (1988) noted, enhancing individual qualities depends on people’s access to social capital. Where Adler and Kwon (2002) have similarly found that access to social capital can support qualities related to actors’ abilities to be successful on the job, a greater emphasis on developing teachers’ social capital can be an important next step in efforts to improve urban STEM education.


Recently, Leana (2011) has pointed out that educators and policy makers have overemphasized improving the individual teacher’s skills and undervalued the benefits of increasing teacher collaboration and overall social capital. She reports on one study in which students showed higher gains in math achievement when their teachers reported frequent conversations about teaching math content with their peers and when there was a feeling of trust among teachers (Leana & Pil, 2006). In a related study, Leana and her colleagues also found a significant interaction between teacher human and social capital (Pil & Leana, 2009). While students of high-ability teachers outperformed students of lower ability teachers, those high-ability teachers who also had strong social capital showed the highest student gains. They also found that students of lower ability teachers performed as well as students of average-ability teachers if the lower ability teachers had strong social capital. These results and the results of our study suggest that for reform efforts to succeed, they need to provide opportunities for teachers to develop social capital. As Leana (2011) suggests:


Building social capital in schools is not easy or inexpensive. It requires time and typically the infusion of additional teaching staff into the school. It requires a reorientation away from a Teacher of the Year model and toward a system that rewards mentoring and collaboration among teachers. . . . But after decades of failed programs aimed at improving student achievement through teacher human capital and principal leadership, such investments in social capital are cheap by comparison and offer far more promise of measurable gains for students. (p. 35)

 

Focusing on developing teachers’ social capital may be even more vital in urban, underserved school districts such as the one where our study took place. Among our teacher participants, 41% had fewer than 5 years of teaching experience. In such districts where teacher turnover rates, attrition, and quality are at greatest risk (Ingersoll, 2011; Ingersoll & May, 2012), strengthening trust and access to expertise can improve the experiences of beginning teachers, which can in turn mitigate those risks. This is particularly salient for STEM education where a wide achievement gap between urban minority students and their suburban counterparts remains (National Academy of Sciences, 2011). However, as we have also highlighted in a previous paper (Baker-Doyle & Yoon, 2011), in some cases expertise is trapped among experienced teachers who have no mechanism for sharing it with other colleagues. If such expertise or human capital remains within the individual teachers, the professional learning benefits to the other teacher-participants—beginning and experienced teachers alike—will be limited. The question then becomes how to create, or encourage the creation of, avenues for sharing and collaboration, such as by using networked learning communities. This has implications for shifting emphases in professional development programs for STEM teachers in these high needs, underserved urban schools where funding for professional development is limited (Blandford, 2012). We posit that alongside more traditional human capital training that aims to improve content and pedagogical content knowledge (OECD, 2011, PCAST, 2010), building teachers’ social capital can more efficiently distribute expertise that can potentially lower the cost of ongoing teacher training.


However, we need more studies to validate the reported findings. It has been suggested that more work needs to be done to explore the effect of teacher social capital, such as through the study of teacher networks (Penuel et al., 2009). And there is even greater need for research that explores the role of teacher social capital in underperforming schools and its relation to student outcomes (Pil & Leana, 2009). We believe that the results of this study, while not generalizable to all educational populations, provides promising evidence in support of further research in this area.


Notes


PISA stands for Programme for International Student Assessment



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Cite This Article as: Teachers College Record Volume 119 Number 4, 2017, p. 1-32
https://www.tcrecord.org ID Number: 21696, Date Accessed: 10/27/2021 6:37:09 PM

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About the Author
  • Susan Yoon
    University of Pennsylvania
    E-mail Author
    SUSAN YOON is Associate Professor of Education at the University of Pennsylvania’s Graduate School of Education with research foci in the Learning Sciences and Science and Technology Education. With funding from the National Science Foundation, her work includes the development of formal and informal curricular interventions with digital visualization tools that model complex scientific phenomena. She also investigates how teachers develop instructional expertise through professional development activities. Collectively, through complex systems and social network theoretical and analytical lenses, her work examines the hidden variables that can significantly challenge learning and instruction. She has published on this work in educational journals such as Science Education, Journal of Research in Science Teaching, and Journal of the Learning Sciences.
  • Jessica Koehler Yom
    University of Pennsylvania
    E-mail Author
    Jessica Koehler Yom is a PhD student at the University of Pennsylvania’s Graduate School of Education and Director of Research at The Future Project, an educational nonprofit. She has worked on various research projects that develop interventions for classroom learning and instruction of scientific content. Her current research interests include adolescent student voice, identity, and engagement.
  • Zhitong Yang
    Educational Testing Service
    E-mail Author
    Zhitong Yang is a research project manager at the Center for Academic and Workplace Readiness and Success at Educational Testing Service. He manages multiple projects focusing on design and implementation of innovative assessment tools to measure cognitive bias, personality, collaborative problem solving, and job performance. His research interests include assessment of science content, noncognitive traits and skills, and collaborative learning. He has presented his research at national and international conferences including the American Educational Research Association and the International Conference of the Learning Sciences.
  • Lei Liu
    Educational Testing Service
    E-mail Author
    Lei Liu is a research scientist with the Learning Sciences Group at Educational Testing Service. She leads multiple projects focused on the design of innovative and technology-rich science assessments that are competency-based and NGSS aligned. Her research has drawn heavily on cognitive and socio-constructivist learning theories with a particular interest in the role of technology in learning and assessing. She has developed simulation-based learning environment and assessments, learning progressions-based assessments, conversation-based assessments, and collaborative problem solving assessments.
 
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