Data-based decision-making in schools: Examining the process and effects of teacher support.

Abstract

The idea of data-based decision-making (DBDM) at the classroom level is that teachers use assessment data to adapt their instruction to students' individual needs and thus improve students' learning progress. In this study, we first investigate this theoretically assumed DBDM process, and second, we evaluate the effectiveness of teacher support on the different steps of this process. Using longitudinal data of N = 120 teachers and their N = 2458 students, we analyzed the relations between teachers' log-file-based data analysis activities, teachers' self-reported frequency of instructional decision-making, and students' learning progress in reading by means of latent mediation analyses. Moreover, we analyzed whether additional teacher support in the form of instructional recommendations, teacher training, and prepared teaching material can promote the implementation of different steps of DBDM. Results of latent mediation analyses revealed positive associations of teachers' data analysis with their instructional decisionmaking and students' learning progress. Teacher support was positively associated with data analysis and students' learning progress (mediated by data analysis). Results are discussed regarding the theoretically assumed steps in the DBDM process and the potential of teacher support with a focus on teachers' instructional decision-making. Educational Impact and Implications StatementProviding teachers with assessment data on student learning progress should lead to a better alignment of instruction to students' needs, thereby improving student achievement. Consistent with this hypothesis, we found that the more teachers engaged with assessment data, the more they reported using them for instructional decisions. In addition, students of teachers who engaged more deeply with assessment data made greater learning gains over the course of the school year. Some of the teachers received additional support to make data-based instructional decisions in the form of instructional recommendations, teacher training, and prepared teaching materials. This support was associated with more frequent data use by teachers, which in turn was associated with higher student learning progress via an indirect effect. Our analyses of the process of data-based decision-making (DBDM) in schools underline that teacher trainings in combination with instructional recommendations and prepared materials have the potential to improve DBDM practice in general education and increase student learning.

Effects of the COVID-19 pandemic on reading performance of second grade children in Germany

“…Förster et al, 2018 ; Müller et al, 2017 ). A rational basis to differentiate and adjust instruction is assessment data on student performance, students’ individual development, and thus their response to instruction (e.g., Förster et al, 2018 ; Hebbecker et al, 2022 ). This places high demands on teachers.…”

“…We checked the missing data pattern and found that a MCAR pattern could not be reasonably assumed based on Jamshidian and Jalal's two-step procedure (Jamshidian et al, 2014). However, when creating 28 equally sized bins for a simple total score at T1 (i.e., T1 reading performance is statistically controlled to a certain degree), we found that in 78.6% of the bins the MCAR assumption could not be refuted (for a similar approach, see Hebbecker et al, 2022). We thus assume that the data follow a MAR pattern, and that the missing data pattern is most likely more on the MAR side of the continuum of missing data patterns between MAR and MNAR (Graham, 2009;Newman, 2014).…”

Co-creating tools to monitor first graders’ progress in reading: a balancing act between perceived usefulness, flexibility, and workload

“…Data-based Decision Making is more dependent on formal data (Wayman et al, 2012;Schildkamp, 2019;Eysink and Schildkamp, 2021;Hebbecker et al, 2022) and generally distinguishes between two types of measures: curriculum-based measurement (CBM) and mastery measures . CBMs are short, standardized measures that indicate the overall skill level.…”

“…After data collection, an analysis phase is needed to convert the raw data into useful information (Klute et al, 2017;Eysink and Schildkamp, 2021). Data can be presented in many forms: tables, texts or graphs (Hebbecker et al, 2022) When curriculum-based measurements is used, pupils' results can be compared to a pre-established cut-off score or the slope can be analyzed to establish whether progress is in line with expectations Oslund et al, 2021). The aim of this analysis phase is to identify pupils' strengths and weaknesses in order to best meet their needs (Eysink and Schildkamp, 2021).…”