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Symposium Overview

Symposium Overview. Bill Penuel SRI International. Complexity and Education. August 2005: Gathering at SFI of scholars in education inspired by how concepts of complex adaptive systems might be applied to education Two areas of study had already begun to develop

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Symposium Overview

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  1. Symposium Overview Bill Penuel SRI International

  2. Complexity and Education • August 2005: Gathering at SFI of scholars in education inspired by how concepts of complex adaptive systems might be applied to education • Two areas of study had already begun to develop • Complexity in education: Helping K-12 students understand complexity • Complexity of education: Studying and modeling educational systems as complex systems • Our symposium focuses on complexity in educational systems • Applications are nascent • We are newcomers to the community • We come bearing theory and preliminary models

  3. Theoretical Frameworks • Social capital theory • Focus on resources and expertise that individuals access through ties to others • Resources and expertise are embedded in networks • New institutionalism • Isomorphism: Emergence of collaborative arrangements as an adaptation to outside pressure • Norms within institutional (professional) fields that are constituted (and sometimes transformed) in local interaction • Complex systems theory • Notion of making explicit assumptions so that systems can be modeled • Application to “systemic reform” efforts

  4. Models and Methods • Penuel and Riel: Integrating social network analysis into multi-level models of school change processes • Joshi: Using shadowing data to examine resources exchanged in interaction and uncovering institutionalized norms • DiBello: Using simulations to help school districts understand the costs of time away from educationally relevant tasks • Sabelli: Analyzing system levels toward developing approaches to cross-case analysis

  5. Agency and Timescales in Education • Within education, there are agents who act at different levels of the system • Students: Classroom, Home, Out-of-School Time • Teachers: Classroom and school • Building leaders: School, district • District administrators: School, district • State and federal policy makers: State, federal agencies • Reform intermediaries: All levels • Institutional change happens on different timescales • Age-graded classroom: 100 years • Data-driven decision-making: 2 years • Adopting a new curriculum: days, months

  6. Applications • Common framework for analyzing social capital in schools • Gathering and interpreting network data • Common data sets • Knowledge building in educational research • Unpacking “professional community” • Cross-case analyses of schools (common language) • Agent-based simulations for researchers • Agent-based simulations for school leaders • What-if scenarios for orchestrating collaboration • Enabling leaders to understand schools and districts as complex, multiscale systems

  7. Teacher Networks and the Diffusion of Innovations Bill Penuel and Margaret Riel SRI International Ken Frank and Ann Krause Michigan State University

  8. A Network Approach to Social Capital Drawing on Portes (1998) and Lin (2001), we define social capital in terms of: • Ties: Interactions among faculty members in a school • Resources and Expertise: The value of resources (e.g., curriculum) and expertise (e.g., wisdom of practice) accessible through ties to others This definition considers social capital as useful for individual action, and secondarily as a social or collective resource. Diffusion of innovations is an emergent characteristic of the school, which is facilitated by teacher talk and sharing of resources about teaching.

  9. A Network Approach to Social Capital For us, analyzing networks is essential to measuring social capital: • Mapping the boundaries of networks and subgroups within networks • Including as part of our models the resources and expertise one can access through those networks • Considering the consequences in terms of changes in teacher attitudes, teaching practice, and student achievement Several scholars have suggested that social capital has a network structure (e.g., Burt, 2000; Lin, 2001), in that valued resources are embedded within a network

  10. A Network Approach to Social Capital • Prior research • Functions of network closure • Resources and expertise flow freely within dense networks • Can protect a network from outside pressure • Functions of bridging • Critical source of new knowledge and skill • People who play bridging functions can exert considerable control over the flow of resources • Implications • Need to attend to network boundaries • Boundaries exist within and across schools

  11. A Crystallized Sociogram

  12. Resources, Expertise, and Consequences • Approaches to conceptualizing resources: • Access to instructional materials • Affordances of particular instructional materials (Stein & Kim, 2006) • Resources-in-use (Cohen, Raudenbush, & Ball, 2003) • Schoolwide norms (Bryk & Schneider, 2002) • Approaches to conceptualizing expertise: • Prior experience with a reform or activity • Adaptive expertise framework (Bransford, Crawford) • Pedagogical Content Knowledge (Ball, Hill) • Formal Preparation (production-function literature) • Teacher experience* • Candidate consequences: • Teaching quality • Curriculum or reform implementation • Student achievement

  13. Our Research Big Questions: • How do interactions with colleagues affect teachers’ beliefs and practices? • What patterns of expertise flow in a school promote the diffusion of innovations across a school? • How do informal interactions combine with professional development and intentional efforts to promote teacher collaboration? • How do teachers choose with whom to interact around practice?

  14. Study of Schoolwide Reforms • Looking at different “home-grown” school-wide reform initiatives • Technology integration • Literacy • Data-driven decision making • Creating standards-aligned assessments • Measured social capital and self-reported influence on practice at two points in time (Penuel, Frank, & Krause, 2006) • We used “implementation levels” at Time 1 as an indicator of expertise • We looked at how getting help from an “expert” influenced teachers reports that their school’s initiative influenced their practice

  15. Sample Characteristics • Schools • 23 schools selected for commitment to collaboration and engaged in whole school reform • 13 elementary; 3 K-8; 5 middle; 2 high schools • 8 high SES <10% free/reduced price lunch • 3 low SES >80% free/reduced price lunch • Teachers • 499 teachers (with matched data for both surveys) • teaching experience: 125 <5 years; 236 6-15 years; 190 teachers 16+ years • School Leaders • 29 informal leaders from 14 of the schools: designated coaches for schoolwide initiatives; generally team or grade-level leaders

  16. Two Analyses for Today • Case studies of two schools’ reforms • A look at the role of critical role of between-subgroup dynamics in a school with respect to flows of resources and expertise • Hierarchical linear model looking at what predicts change in instructional practice • A look at between andwithin-subgroup dynamics and their influence on teachers’ attitudes

  17. Glade and Crosswinds • Two schools in California’s II/USP Program • Similar demographics and challenges in improving literacy outcomes for English Language Learners • Adopted similar approaches to reform: Promoting teacher community facilitated by instructional coaches • Coaches were expected to play slightly different roles • Two schools met with dramatically different results: • Glade: Still struggling for reform to take hold and gain legitimacy • Crosswinds: Steady gains in achievement, strong shared commitment to reform goals • Comparative case study analysis set out to test rival explanations (Yin, 2003) for why

  18. Formal Meetings at Glade and Crosswinds

  19. Density of Interaction

  20. Glade’s Subgroups Veteran Group New Immigrant Teacher Group

  21. Crosswinds’ Subgroups Early Elementary Group

  22. Access through Ties to Expertise Size and color indicate extent of use at time 1 B provides help to C provides help to A provides help to D

  23. HLM Analyses of Innovation Diffusion • Models take into account expertise of colleagues with whom teachers interact • Three levels to model • School • Subgroup • Individual teacher

  24. Access through Ties to Expertise Ripple around A indicates increase in use between time 1 and time 2 B provides help to C provides help to A provides help to D Change in A is a function of interaction with people with expertise; the greater the mean expertise of Helpers B, C, and D, the greater the change in A

  25. 3-Level Model of Change in Practice

  26. Discussion • The case study results point to the importance of the informal network in supporting or inhibiting the flow of resources and expertise across subgroups • Relationship to school (i.e., perceived collective responsibility) is filtered by experience within subgroup (i.e., fit with subgroup). • The HLM analyses are suggestive of a way that between andwithin-subgroup dynamics can induce changes in teachers’ practices.

  27. The Role of Institutionalized Norms of Autonomy and Equality in Shaping Interactions of Teachers Aasha Joshi William R. Penuel SRI International

  28. Institutionalized Norms • Norms emerge out of interactions • “Specify how things should be done…designate appropriate ways to pursue them [goals or objectives]” (Scott, 2001, p55)

  29. Equality as a Norm in Schools • Network configuration • Many ties within and across subgroups • No single leader • Direction of help • Bias against seeking and giving help • Interaction structure • Collaborative “war” stories • Experience-swapping • Simply ignore reform (don’t believe there is anything to learn) • Focus of talk • Common challenges and struggles (e.g., students) • Shared tasks

  30. Autonomy as a Norm in Schools • Network configuration • Limited interaction among colleagues (Lortie, 1977) • Direction of help • Limited evidence of any kind of helping interaction • Interaction structure • Experience-swapping • Collaboration as distribution of responsibility for tasks • Focus of talk • Talk about broad principles (e.g., standards) but not much about teaching

  31. Creating Disequilibrium: Efforts to Create Teacher Community • Network configuration • Informal and formal leaders emerge in bridging roles • Teachers also perform bridging roles to colleagues • Direction of help • Creates press to give and receive help • Interaction structure • Apprenticeship learning (e.g., model teaching) • Focus of talk • On teaching and on challenging and critiquing peers’ practice

  32. Shadowing as a Way to Study Transformation of Norms in Interaction • The shadowing task: • Single researcher followed shadowees from arrival to the school until the end of their work day; debrief interview following the shadowing day • Documentation: • Duration • physical location (e.g., staff room, hallway, or telephone) • intentionality (e.g., scheduled or impromptu) • Topics • Participants • Comments by the participants during the interactions • Data set: • 6 schools • 14 teachers • 6 school leaders • Coded: 798 interactions

  33. Case Study Schools

  34. Case Study Schools

  35. Network Configuration

  36. Direction of Help • Bi-directional help is common among both school leaders and teachers • School leaders give help more than they receive help • Teachers receive help more than they give help

  37. Interaction Structure • Modeling as help is rare; talk as help is most common

  38. Focus of Interaction • Instruction, coordination of activities, and school-level problems are common topics of talk as help

  39. Discussion • Network configuration, helping patterns show some evidence of perturbations with respect to norms of autonomy and equality • Networks are dense in schools, and interaction among teachers is frequent, both in planned and impromptu encounters • There is some asymmetrical help, with designated leaders providing it • In these schools at least, the nature of interaction and focus of talk there is evidence of equality and autonomy norms • Lots of “experience swapping” remains • Talk about instruction happened, but was not the main focus of talk

  40. Complex Systems and Educational Change Nora Sabelli, SRI International Jay L. Lemke, University of Michigan

  41. Balcones Conference • 2001: Meeting of 4 major NSF-funded projects in which researcher-educator partnerships had sustained successful collaborations for over 10 years • Guiding Questions: • What is the relationship between educational system, research on the system, and the models of change used in designing the work? • What are the open problems with the existing models that could shape the future of this type of work? • Does the existence of models of change facilitate scaling and adaptation of reform efforts? • Is there a taxonomy of such models where these projects and others have generated knowledge?

  42. Internal and External Complexity of Complex Adaptive Systems • A complex adaptive system is situated in an environment: • That environment is always more complex than the system itself, and therefore, it can never be completely predictable for the system, but the system depends on regularities of the environment for maintaining [the] energy supply needed to support its internal structures and processes . SFI Working Paper Abstract; 2003 Author: Juergen Jost Paper # 03-12-070

  43. Changes in Paradigms Based on Complex Systems Theory • Causal loops vs. causal chains (nonlinear networks) • Integrated systems vs. isolable units of analysis • Dynamical models and simulations vs. input-output modeling • Unique systems vs. generic systems • Emergence vs. determinacy (surprise)

  44. Complex systems theory provides athinking tool for: • Qualitative reasoning about complex socio-natural systems • Making the infrastructure (human and technical) assumptions, needs and opportunities more explicit. • Quantitative modeling and simulation

  45. Complex Systems Theory Provides a Thinking Tool For • Qualitative reasoning about complex socio-natural systems • Making the infrastructure (human and technical) assumptions, needs and opportunities more explicit. • Quantitative modeling and simulation

  46. Creating a Possible Framework to Make the Infrastructure Visible • Capacity to order and simplify • Identification of significant features • Congruence with reality • Communicative power • Explanation of a total process • A basis for inquiry and research: • How to build a model that specifies the relationships between concepts

  47. Goal: Cross-case Research • It takes a village to study a village: who’s on the team? • The education system is a system • Cross-project cumulativity of cases: meta-models for research • Education is local, research is general • One example of a meta-model • To annotate local case studies

  48. Goal: Cross-Case Research • It takes a village to study a village: who’s on the team? • The education system is a system • Cross-project cumulativity of cases: meta-models for research • Education is local, research is general • One example of a meta-model • To annotate local case studies

  49. How is Learning Organized? Which people learn (equity) Why people learn (context) How people learn (cognition) What people learn (content) Transition across Levels Standards Demographic Trends Standards Instructional workforce Coherence & Accountability Teacher Recruitment and policies Teacher Certification Standards Standardized Testing System options and constraints Incentives Instructional Leadership and coherence Local education needs Distribution of internal and external resources Incentives Alignment Teacher Professional Development Available resources Teacher Expectations Assessment data available Pedagogical Content Knowledge

  50. How is Learning Organized? NSF Systemic Change Drivers Cohen et al. Confrey et al. Incentives and Accountability What is Known about Learning Standards-based Curriculum Evidence Incentives Resources Coordination Accountability Student Outcomes Instruction Standards-based curriculum Professional Development Why people learn (context) Which people learn (equity) How people learn (cognition) What people learn (content)

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