- Year 2023
- NSF Noyce Award # 2150905
- First Name Shannon
- Last Name Campe
- Discipline Computer Science
Jasmine Ma, New York University; Laura Bostick, Louisiana Tech University; Matin Pirouz Nia, California State University-Fresno
There is increasing demand for STEM teachers particularly in high-need schools and the call for K-12 computer science (CS) teachers, notably those who are prepared to engage students from underrepresented groups, has grown exponentially in the last ten years. Yet retention is particularly low among CS teachers. This study will look at how teacher preparation programs can increase CS teacher retention in high-need schools and prepare teachers to deliver effective, equitable CS instruction. This study will look at how teacher preparation programs can increase CS teacher retention by persisting in completing the preparation program and 2-year teaching requirement for Noyce Scholars. The goal is to generate information that can be used to increase the longer-term retention of CS teachers in high-need schools as well as identify the features of programs that prepare teachers to deliver effective, equitable CS instruction. A design-based research approach will be used to iteratively apply the results to three Noyce Scholars programs to increase CS teacher retention and effectiveness.
The research questions guiding this study are: 1. How can teacher preparation programs increase the retention of CS teachers in high-need schools? What individual-level factors play a role (e.g., beliefs, knowledge, confidence)? What interpersonal factors play a role (e.g., coaching, mentoring, community of teachers)? What institutional factors at the school and within the preparation program play a role (administrator support, financial resources/supplies, course content)? 2. How can teacher preparation programs prepare teachers to deliver effective and equitable computer science education in middle and high school? What are the different ways that Noyce Scholar programs train teachers on CS and social justice content knowledge and pedagogical content knowledge? To what extent does the training help teachers deliver effective and equitable CS education?
The research will be guided by socio-ecological and social justice pedagogical content knowledge theoretical frameworks. The study will involve up to 80 teachers who are being trained to teach CS in middle and high schools. Mixed methods will be used to collect survey, interview, observation and institutional data over time. The data will be analyzed, using an inductive case study approach, to describe teachers’ development of CS knowledge and social justice pedagogy, their experience as a student teacher and a novice lead teacher, their community of support, their identity as a CS teacher, as well as features of the preparation program and school sites. An initial conjecture map documents research-based hypotheses about how to prepare teachers to persist and engage in effective CS instruction. Using a design-research approach, the team will use the data to refine the conjecture map and make changes at the program level.
During the first year of this 5-year Robert Noyce Track 4 Research grant, activities have focused on: building relationships within the team, creating Scholar and faculty instruments (surveys, interviews, observations) to measure the above-mentioned constructs, obtaining IRB approval for all procedures and instruments at ETR and partnering universities, recruiting and training research assistants at each university and recruiting Noyce Scholar CS pre-service teacher participants from university Track 1 cohorts. Data has been collected from Scholar participants at two of the three universities. Recruitment has been slow due to challenges the university programs are facing around their own Track 1 recruitment, but we have enrolled 5 out of 6 eligible participants at two of the three sites, of which we have collected survey and interview data from during their preparation (coursework and student-teaching) phase. We are also currently working with each university to obtain course information by collecting syllabi and conducting faculty interviews for all courses relevant to our study. We have done some preliminary analysis and will continue with more, including triangulating participant and course data, to describe each Scholar’s experience (building their case study), inform revisions to our conjecture map and feed back to participating sites to support programmatic changes that could increase the teaching of equitable and inclusive CS and the retention of CS teachers in high-need schools.
In Year 2 we will continue with recruiting new participants and collecting preparation phase data as well as working with our first cohort of participants to collect data during their induction phase (2-year teaching in their own classroom at high-need school). Additional course data will be collected and faculty interviews conducted based on university program changes. We will continue our cycle of analysis, triangulating the varied data sources, to build out stronger case studies of each participant as well as those clustered within each university.
Access to CS education is disproportionately distributed across public schools. High-need schools have greater numbers of students from groups that are underrepresented in computing fields; they are also more likely to have teacher turnover and less likely to have experienced teachers and CS opportunities. But little is known about the different ways that equity and inclusion are conceptualized and operationalized in teacher preparation programs, including those for Noyce Scholars from different STEM disciplines. This study involves a multidisciplinary team of investigators who are positioned to identify strategies that prepare teachers to create equitable and inclusive CS classes for students in these schools. It will also identify strategies for training and support in teacher preparation programs that can increase teacher retention. The geographic dispersion of Noyce projects across both rural and urban areas will increase the likelihood that the results will be relevant to a range of populations and settings.