Participant selections were informed by conversations with subject matter experts, analysis of OSPI workforce data, and the relevant literature. In selecting districts to interview, we targeted districts with well-established programs and diversity of dual language program characteristics (relative to each other). Our objective in selecting programs that were well-established was to speak with districts who had had time to figure out what worked for them and what did not when hiring for dual language programs.Most of our districts were also in either suburban or city locales, as defined by the National Center for Education Statistics (2018).
The interviews we conducted were semi-structured. After transcribing the first three interviews, our research team split up and coded them separately using emergent thematic coding. We then came back together to discuss the codes that had emerged from our initial analysis and finalize the first draft of our codebook. Interviews were then exchanged between researchers so that each initial coding could be peer reviewed and re-coded to fit the emerging thematic coding paradigm.
The fourth, fifth, and sixth interviews were coded against the first draft of our codebook, while still leaving space for new codes to emerge. Coding of the fourth, fifth, and sixth interviews was performed by one researcher and peer-reviewed by a second. New codes were then discussed between the researchers to finalize the codebook. With the codebook finalized, the first three interviews were then re-coded for new codes that had emerged in the second round of coding. Through the process, any conflicts of opinion were brought to the group and resolved by consensus.
The findings in this report focus on tactics we identified that were specific to dual language workforce recruitment but supports, challenges, and non-workforce tactics were coded to help us contextualize the workforce tactics and possibly inform future research.
To help enrich our qualitative findings, we were granted access to workforce data from the Washington State Office of Superintendent of Public Instruction (OSPI). The data set we used contains workforce data for every teacher employed at a Washington public school from the 2010-2011 to the 2019-2020 school year (or ten years), and contains information on teacher demographics, employment location (school &district), what endorsements and certifications each teacher has, what they are assigned to teach, and which higher education or state certifying agency recommended them for certification.
One big gap in this data, for this project, was not knowing whether a teacher was teaching in a dual language program. This designation was missing from the data set and limited the depth of analysis we were able to perform. OSPI is currently in the process of enacting a plan to flag dual language courses, which will allow teachers in these courses to be identified as dual language teachers (P. Finnegan, personal communication, May 19, 2020).