The development of digital technologies has significantly influenced education, accompanied by the generation of large volumes of data (McCarthy et al., 2023). The use of data can help teachers to narrow academic performance gaps among students (Gullo, 2013). However, the challenges of teacher’s use of data in the classroom still exist (Reeves et al., 2016), including a struggle to effectively analyze data (Garner et al., 2017). These challenges highlight the need for structured approaches that support teachers in integrating data into their instruction. There is another significant limitation in the current research on data-driven instruction, which is the lack of a clear and unified conceptual framework to understand how teachers utilize data in their instruction and what kinds of professional knowledge or skills are needed. While scholars have explored teachers’ data use and proposed theoretical models concerning data literacy and data-driven decision-making (Gummer & Mandinach, 2015; Mandinach, 2012; Mandinach & Gummer, 2016), some crucial gaps still remain that present a comprehensive understanding of instruction. The lack of attention on the perspective of instruction design framework also leads to the neglect on what types of data actually used by teachers in instructional practice. Moreover, although contextual factors have been identified as influential to data usage (Huguet et al., 2014), these factors are rarely explicitly connected to the practice of teachers’ data-driven instructional practices. Therefore, the instruction design theoretical framework that employs systematic models and a broad set of principles to guide the planning and implementation of effective instruction is needed (Abuhassna & Alnawajha, 2023). The theoretical framework of instruction design can inform us of the various components of instruction, thereby facilitating the analysis of the instructional components of Data-driven Instruction Competence(DDIC). At the same time, this study will also be based on the competence theoretical framework of teachers. Competence is a multidimensional construct encompassing not only the knowledge and skills of teachers but also attitudes (Blömeke et al., 2015). Based on this framework, DDIC not only includes knowledge and skills, but also should possess attitudes or beliefs and so on. Specifically, this research will explore the following questions: (1) What is the definition of DDIC? (2) What types of data are addressed in existing studies when describing teachers’ data use in instructional practice? (3) What contextual factors influence DDIC? (4) What instruments are used to assess teachers’ DDIC? Based on the instruction design framework and the competence framework, this study focuses on primary and secondary school teachers, aiming to define the DDIC of teachers, analyze the specific data types that teachers adopt in their instructional practices, identify the factors that influence teachers’ DDIC in educational contexts, and explore the instruments used to assess teachers’ DDIC. This provides a more instructional and contextual perspective to understand how teachers’ use data in their instruction, thereby equipping teachers with targeted support to effectively integrate data into their instructional practices in educational contexts.

Systematic reviews are suited for answering specific research questions by collating evidence that fits eligibility criteria (Liberati et al., 2009). This research was mainly based on the method of systematic literature review, and was guided by PRISMA (preferred reporting items for systematic reviews and meta-analyses) (Moher et al., 2009). The search strategy utilized key terms related to DDIC. Multiple electronic databases in both Chinese and English were included to explore studies from diverse research contexts. The following electronic databases were selected and searched to conduct this systematic review: Scopus, ProQuest (ERIC & Education database) and CNKI (China National Knowledge Infrastructure). The inclusion criteria for screening the literature in this study were as follows: (1) Research focus: focusing on DDIC; (2) Research subjects: primary and secondary school in-service teachers; (3)Publication time: from 2005 to 2025; (4) Publication type: peer-reviewed journal papers; (5) Language: Chinese and English; (6) Research type: empirical research; (7) Availability: full text is available. This study also followed the procedures and methods of systematic review research. Following initial system development, researchers conducted coding and revision, reached consensus on disputed cases, and completed analysis with quality evaluation. The initial search across three databases yielded 1121 articles, which were reduced to 995 after duplicate removal. Following title and abstract screening, 114 peer-reviewed journal articles were retained for full-text eligibility assessment. Finally, 19 studies were included in the analysis.

This study adopts the method of systematic literature review, applying the instruction design and competence theoretical framework to explore research questions. In the section of components, DDIC encompasses not only knowledge and skills but also attitudinal components. Even though teachers apply student data in their instruction, they mainly applying it to implementing instructional assessment (Abrams et al., 2016). Assessment is part of teacher’s instructional practices, but DDIC’s definition goes far beyond this. Data should not be solely used for instructional evaluation purposes, and more attention should be paid to non-academic performance data as well. Regarding the types of data used by teachers, data on students’ academic performance is relatively common, and this conclusion is similar in both China and other countries. In the factors influencing teachers’ DDIC in the educational context, the construction of facilities and culture are the two main influencing factors. However, the relationship between these factors and teachers’ data-driven instruction, particularly their DDIC, still remains underexplored. The current assessment instruments used for DDIC mainly rely on questionnaires and interviews, which indicates that assessment instruments used for DDIC focus on a relatively narrow range of aspects. This systematic review explores various types of data used by teachers in their instructional practices, the definition of DDIC, the contextual factors influencing DDIC, and the assessment instruments used for DDIC in existing studies.

Abrams, L., Varier, D., & Jackson, L. (2016). Unpacking instructional alignment: The influence of teachers' use of assessment data on instruction. Perspectives in Education, 34(4), 15–28. https://doi.org/10.18820/2519593X/pie.v34i4.2 Abuhassna, H., & Alnawajha, S. (2023). Instructional Design Made Easy! Instructional Design Models, Categories, Frameworks, Educational Context, and Recommendations for Future Work. European Journal of Investigation in Health, Psychology and Education, 13(4), 715–735. https://doi.org/https://doi.org/10.3390/ejihpe13040054 Blömeke, S., Gustafsson, J.-E., & Shavelson, R. J. (2015). Beyond Dichotomies: Competence viewed as a continuum. Zeitschrift für Psychologie, 223(1), 3–13. https://doi.org/10.1027/2151-2604/a000194 Garner, B., Thorne, J. K., & Horn, I. S. (2017). Teachers interpreting data for instructional decisions: where does equity come in? Journal of Educational Administration, 55(4), 407–426. https://doi.org/10.1108/JEA-09-2016-0106 Gullo, D. F. (2013). Improving Instructional Practices, Policies, and Student Outcomes for Early Childhood Language and Literacy Through Data-Driven Decision Making. Early Childhood Education Journal, 41(6), 413–421. https://doi.org/10.1007/s10643-013-0581-x Gummer, E. S., & Mandinach, E. B. (2015). Building a Conceptual Framework for Data Literacy. Teachers College Record, 117(4), 1–22. https://doi.org/10.1177/016146811511700401 Huguet, A., Marsh, J. A., & Farrell, C. (2014). Building teachers' data-use capacity: Insights from strong and developing coaches. Education Policy Analysis Archives, 22. https://doi.org/10.14507/epaa.v22n52.2014 Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ, 339, b2700. https://doi.org/10.1136/bmj.b2700 Mandinach, E. B. (2012). A Perfect Time for Data Use: Using Data-Driven Decision Making to Inform Practice. Educational Psychologist, 47(2), 71–85. https://doi.org/10.1080/00461520.2012.667064 Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366–376. https://doi.org/10.1016/j.tate.2016.07.011 McCarthy, A. M., Maor, D., McConney, A., & Cavanaugh, C. (2023). Digital transformation in education: Critical components for leaders of system change. Social Sciences & Humanities Open, 8(1), 100479. https://doi.org/https://doi.org/10.1016/j.ssaho.2023.100479 Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ, 339, b2535. https://doi.org/10.1136/bmj.b2535 Reeves, T. D., Summers, K. H., & Grove, E. (2016). Examining the landscape of teacher learning for data use: The case of Illinois. Cogent Education, 3(1), 1211476. https://doi.org/10.1080/2331186X.2016.1211476