Data-informed decision-making [DIDM] is a complex and iterative process in which teachers use various educational data to transform, adapt, and improve their instructional practices (Datnow & Hubbard, 2010; Mandinach & Gummer, 2016). Based on several frameworks for data use and data literacy for teaching (e.g., Hansen & Wasson, 2016; Lai & Schildkamp, 2013; Mandinach & Gummer, 2016; Schildkamp & Ehren, 2013; Saar et al., 2022), the process of DIDM generally requires teachers to identify a relevant and context-specific problem of practice, pose meaningful inquiry questions, formulate hypotheses, collect, analyze, and interpret the data within the instructional design, and make a decision through reflection and evaluation. Teachers also combine their pedagogical expertise with the necessary knowledge, skills, and dispositions to effectively utilize data. 

However, studies revealed that teachers struggle with some DIDM components, such as formulating meaningful and measurable purposes for data use and analyzing and describing the data quality (Ebbeler et al., 2017; Kippers et al., 2018). Although teachers have a high level of awareness about data, adopting the steps of DIDM may be poor, resulting in no instructional improvement (Gelderblom et al., 2016). Moreover, teachers may have a narrow mindset of data being limited to only test results or quantitative data (Dunn et al., 2019). However, the evidence for tracking and improving learning can be collected in multiple ways, including formal and informal data (Nitko & Brookhart, 2014). Besides, teachers need to triangulate across various data sources to prevent confirmation biases (Mandinach & Schildkamp, 2021). Therefore, educational data is a holistic concept that includes input, process, outcome, and contextual data (Ikemoto & Marsh, 2007; Lai & Schildkamp, 2013), encompassing both traditional and sophisticated data from learning dashboards or learning management systems (Mandinach & Gummer, 2016). Yet, given the deluge of data, how teachers make sense and construct interpretations based on it is still debated (Campos et al., 2021).

Research in DIDM has been predominantly shaped by European studies, particularly in the Netherlands and Belgium, as well as by contributions from the United States and New Zealand (Madinach & Schildkamp, 2021). Consequently, many professional development [PD] initiatives to support teachers' data use for instruction, grounded in studies conducted in these contexts, are built around three fundamental design principles: (i) establishing a foundational understanding of educational data and its purposes; (ii) providing opportunities to apply the data use process in real classroom settings to enhance instruction; and (iii) fostering a data-informed culture within schools (Ansyari et al., 2022). Furthermore, teachers should be involved in collective activities to make sense of data (Mandinach & Schildkamp, 2021). Hence, PD interventions focusing on data teams (e.g., Ebbeler et al., 2016; Schildkamp & Ehren, 2013), discussion groups (e.g., Lai et al., 2025), or teacher-researcher collaboration (e.g., Saar et al., 2022) provide insights for designing effective learning environments for teachers; however, there is still a gap for how these kinds of designs function for Turkish education settings. So, this study positions such collaborations as a powerful way to promote PD by drawing upon Wenger’s (1998) Communities of Practice [CoP], in which members come together around a shared domain of interest to make a change or find solutions in their practice (Wenger et al., 2015). Considering this, the novelty of this study lies in formulating and executing design principles based on the integration of CoP and DIDM frameworks adapted to the emerging context of data use in Turkish education. The overarching research question of the study is as follows: “How can a "teacher PD model based on CoP focusing on data use processes support teachers to make data-informed decisions in classroom practices?”

This study adopts Design-Based Research [DBR] to foster teacher learning and develop domain-specific design principles for data use in instruction. DBR is an iterative framework addressing educational problems in authentic settings while generating usable knowledge and theories of learning and teaching (Bakker, 2018). Following McKenny and Reeves’ (2012) DBR phases- analysis and exploration, design and construction, and evaluation and reflection-the study consists of two meso-cycles: Study 1 and Study 2. Study 1 began with a systematic literature review and semi-structured interviews with primary and middle school teachers to explore their data use practices and needs. The qualitative content analysis of these interviews informed the initial design principles. Nine primary and middle school teachers voluntarily participated in weekly online PD sessions from March to June 2024. Each session was adapted to teachers' needs. Data for evaluation included transcriptions of online sessions, focus group interviews, weekly feedback, teacher artefacts, and a researcher diary. Deductive thematic analysis was used to assess how PD contributed to teachers’ understanding of data, instructional practices, and the development of a teacher community. Study 2 was built upon Study 1 findings and revised design principles. It involved 11 middle and high school teachers from a private school. While PD sessions continued, the study also aimed to explore how data use for instruction is predicted by several variables such as user characteristics, school organizational characteristics, collaboration, and data characteristics. These variables were identified in European studies (Hase et al., 2022; Schildkamp et al., 2014) on factors influencing data use. To investigate these relationships, the Data Use Survey (Schildkamp et al., 2017) and the Data-Driven Decision-Making Efficacy and Anxiety Inventory (Dunn et al., 2013) were adapted to the Turkish language and culture. Ethical approval was obtained, and the study remains ongoing. Weekly meetings serve as both a PD space and a data collection process while validity and reliability evidence of the adapted scales continue to be gathered. This DBR study not only refines a PD model for teachers’ data use but also examines contextual variables predicting data-informed instruction. Integrating qualitative and quantitative methods to support methodological triangulation will contribute to both theoretical insights and practical strategies for fostering teacher learning in different educational settings.

In the first cycle, findings indicated that teachers' data use remained largely traditional, focusing on summative assessments and classroom observations rather than a more integrated approach involving diverse data sources. Contextual factors such as limited access to diverse data sources in public schools, students' restricted access to the internet and digital devices, and time management challenges hindered the data use process and its integration into instructional decision-making. Given these limitations, the second cycle was designed to explore data use in a context where teachers have access to diverse data sources and structured digital tools. An additional research question was introduced to achieve this: "What are the current data-use practices of teachers who already have access to diverse data types and technologies?" A private school was selected for this phase, as it employs a comprehensive data system where administrators and teachers regularly collect, analyze, and use student-related data. All students are also part of this system and have unrestricted access to digital tools. This context aligns well with the study's central motivation of adopting a more holistic view of data, which incorporates various types. The second cycle is ongoing, and qualitative and quantitative data collection will be completed in the following months. The multiple regression analysis will provide insights into how user characteristics, school organizational characteristics, collaboration, and data characteristics predict teachers’ data use for instruction. Findings will contribute to refining the PD model and generating further design principles for enhancing data-informed instructional practices.

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