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Building Teacher Capacity for Equity-Centered Pedagogy through STEM Data Literacy Learning

  • Year 2022
  • NSF Noyce Award # 1950340
  • First Name Eugene
  • Last Name Chiang
  • Discipline Other:STEM
  • Co-PI(s)

    Elisa Stone

  • Presenters

    Elisa Stone, Allison Firestone, Sara Baring, & Gabriela Bravo-Lopez, UC Berkeley

Need

Fluency with collecting, analyzing, and interpreting data is increasingly critical for STEM teachers and their students to enable them to address a broad range of complex questions arising in today’s world. The “Berkeley-3D” Noyce project introduces a special focus on STEM data literacy and data science teaching and learning, addressing a crucial need in today’s educational landscape. Using a 3-dimensional program design for deeper learning, teacher candidates gain knowledge of STEM data literacy subject matter and curriculum goals, knowledge of data literacy teaching and content pedagogy, with a focus on teaching diverse learners, and knowledge of student learners of data literacy.

Goals

The primary research questions that drives our work are, What is the nature of STEM data literacy-oriented classroom practices for Berkeley 3-D student teachers and recent graduates? To what extent are these teachers guide their students to explore social- and racial-justice in their math and science learning?

Approach

We collect quantitative and qualitative data from four sources: (a) interviews of students and graduates; (b) interviews of course instructors; (c) the Annual Spring Berkeley Teacher Preparation Program Survey; and (d) observations of course sessions and alumni-attended and facilitated professional development workshops focused on data literacy and STEM. We are engaging in data collection through interviews with a longitudinal approach, beginning when participants complete the program as student teachers and re-interviewing them at the end of each year that they teach. Our data collection focuses on capturing teachers’ reflections on data literacy practices, understanding and implementation of data literacy in their content areas, their willingness and ability to integrate social/racial justice issues into data literacy instruction, and their perceptions of preparedness for doing so. We code responses for key themes related to STEM data literacy teaching and learning; this approach drew upon Miles et al.’s (2020) systematic approach to qualitative analysis. In doing so, we applied a thematic approach, which occurs in iterative phases: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and producing a report (Braun & Clarke, 2012).

Outcomes

We have found that teacher preparation program graduates had moderate perceptions of preparedness in their ability to enact STEM data literacy instruction in their content areas. Results suggest that teachers would benefit from more examples and supported practice opportunities to build pedagogical content knowledge from existing content knowledge and more support in integrating social/racial justice into data literacy. These findings indicate areas of strength and areas where further attention is warranted to improve outcomes for program graduates.

Broader Impacts

The teacher education programs for Berkeley-3D project have been successful at recruiting more diverse group of pre-service teachers. Many of our students are motivated to teach by an interest in giving back to the urban and rural communities from which they come, and teaching in the high-needs schools in which they do their fieldwork. Our partner districts serve a large and extremely diverse population of students, many of whom are underrepresented minority students, English Language Learners, and/or qualify for free or reduced-priced lunch. An important benefit of the Berkeley-3D program is its contribution to increasing the diversity of the K-12 teacher population, especially in the Bay Area and San Joaquin Valley. These teachers bring high-quality math and science learning to the increasingly diverse student populations within these regions. We will contribute to best practices in STEM teacher education, particularly in data literacy teaching and learning, by sharing findings from our research on our graduates with the national teacher education community.

URLs

https://calteach.berkeley.edu/

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This material is based upon work supported by the National Science Foundation (NSF) under Grant Numbers DUE-2041597 and DUE-1548986. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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