The What and How of Supporting Teachers: Experienced Teacher-Facilitator Perspectives to Teach Algorithmic Injustices Within High School Computing Classrooms (Poster 1)

• In Event: Opportunities and Challenges in Teaching Algorithmic Justice in K–12 Computing Education

Abstract

Objectives: We present findings from our preliminary analysis of participation of twelve teacher-facilitators in a series of co-design sessions to answer (1.) What did experienced teachers identify as needs of teachers new to teaching critical AIML within introductory high school computing classrooms? And (2.) How do they think we can better support new teachers towards that end?
Theoretical framework: Our theoretical framework includes theories from critical computing education (Kafai & Proctor, 2021; Ko et al., 2020), teacher learning (Darling-Hammond & Richardson, 2009), and co-design with teachers (Bonsignore et al., 2017; Severance et al., 2016). Teachers are agents of change in classrooms. Most efforts such as teacher preparation programs, curricular interventions, or professional development sessions focus on supporting teaching practice. And yet, we know very little about the support teachers need to teach AIML, not as neutral abstract concepts but as deeply connected to societies and communities, highlighting how AIML tools and algorithms are shaped by different axes of oppression within societies (e.g., Benjamin, 2020; Ebanks, 2018).
Methods & Data Sources: We facilitated eight (90 min. each) co-design sessions with experienced high school computing teachers to brainstorm how teachers new to the Exploring Computer Science (ECS) program can be supported to teach critical AIML (Goode, Chapman, & Margolis, 2012). These teacher-facilitators have taught ECS for 5+ years and facilitated teacher professional development sessions for 3+ years. The co-design sessions (October 2022 - May 2023) focused on social justice issues and their connections to AIML with several opportunities for teacher-facilitators to brainstorm how to support new teachers in adopting it in high school classrooms. We collaboratively analyzed teacher notes from the sessions and inductively generated themes (Miles & Huberman, 1994), practicing Small and Calarco’s (2022) guidelines for rigorous qualitative work by working towards extended exposure, developing cognitive empathy with the participants, aiming for heterogeneity and palpability, conducting follow-up, and reflecting throughout the data collection and analysis process.
Results: Overall, teacher-facilitators identified the need to support new teachers with a range of material and ideational resources and brainstormed ways of scaffolding novice teacher learning. The teacher-facilitators highlighted the need to engage new teachers about biases and injustices within algorithms shaping the AIML tools, implications of large-scale computation for natural resources such as water and minerals, and support teachers with ample opportunities to reflect on their positionalities and relationalities in order to have deeper and authentic conversations with their students. Further, teacher-facilitators called for three main ways in which new teachers can be supported: by modeling student learning experiences for teachers during professional development sessions, allowing for teachers to reflect on their classroom practice and opportunities to engage with different axes of injustices in AIML, and providing related material and ideational resources for teacher enrichment and empowerment.
Significance: Findings from this analysis of co-design sessions shed light on both the “what” (resources and opportunities) and the “how” from experienced teachers’ perspectives as future design and research efforts work on supporting teachers to include algorithmic injustices within their classrooms.

Authors

• Gayithri Jayathirtha, University of Oregon

• Gail Chapman, University of California - Los Angeles

• Joanna Goode, University of Oregon