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The purpose of this logistic regression study was to identify predictors of teacher-reported sustained use of data-based instruction (DBI) during the COVID-19 pandemic and assess the extent to which the identified predictors explained teachers' sustained use after completing programmatic support for intensive early writing instruction. We surveyed 58 teachers who participated in a professional development efficacy trial regarding their sustained use of DBI in writing. The model indicated a higher predicted probability of sustaining DBI for teachers who received the full treatment (tools, learning modules, and coaching for 20 weeks of intervention during their year of participation in the efficacy trial) compared to teachers in the control group who only received tools and learning modules at the end of their participation year. In addition, teachers who taught in-person were more likely to sustain compared to those who taught in remote or hybrid models, controlling for other variables. Furthermore, as the number of facilitators that teachers reported increased, teachers were more likely to sustain their use of DBI components, controlling for other variables. Further research could shed light on the relative impact of different types of facilitators and different levels of support. [ABSTRACT FROM AUTHOR]

Despite decades of research efforts, data-based instruction (DBI) for students with intensive intervention needs are not being widely used in practice as anticipated, and many educators have difficulties in implementing it. This systematic review aimed to examine what kinds of implementation drivers and strategies have been used to support educators implementing DBI and what kinds of implementation outcomes researchers have measured. Eighteen studies were synthesized using the Implementation Drivers framework and Implementation Outcomes taxonomy and were quality appraised. We found that the majority of studies primarily used competency drivers to increase teachers' DBI expertise, while a limited number of studies focused on organizational and leadership drivers. Acceptability and fidelity were frequently assessed as implementation outcomes. We discussed the implications of the findings, including the need for researchers to incorporate implementation drivers and outcomes at diverse levels to best support educators' implementation of DBI, as well as the limitations of this review, such as the limited generalizability of the findings. [ABSTRACT FROM AUTHOR]

In a multiyear, multisite, randomized control trial, we examined the effects of comprehensive professional development designed to support teachers’ data-based instruction (DBI) for students with intensive early writing needs. Teachers (N = 154; primarily special educators or intervention specialists) were assigned randomly to a treatment group (n = 76), in which they received tools, learning, and coaching to support their DBI implementation over 20 weeks, or to a control group (n = 78). Students either received DBI in early writing (n = 155) from treatment teachers or their usual writing instruction (n = 154) from control teachers. Treatment teachers outperformed controls on measures of DBI knowledge and skills (d = 1.57) and self-efficacy for writing instruction (d = .94), and treatment students outperformed controls on proximal and distal writing outcomes (ds = .14–.29). Student characteristics (grade, special education status, English learner status, and race/ethnicity) did not moderate intervention effects. We discuss findings in terms of the importance of supporting students with intensive learning needs, the efficacy and feasibility of implementing DBI-TLC, and implications for pre- and in-service teacher training and support. [ABSTRACT FROM AUTHOR]

For students with intensive learning needs for whom standard, validated interventions do not effectively promote academic growth, data-based instruction (DBI) is suggested as an effective, fine-grained approach to individualization. Key to DBI's success is making instructional changes based on individual students' progress monitoring data. The purpose of this study was to evaluate the effects of such instructional changes on student early writing outcomes. We applied a piecewise linear–linear mixed-effects (PLME) model to determine student writing growth trajectories before and after teachers introduced instructional changes. Using data from 46 elementary school students with intensive writing intervention needs, results showed that a PLME model with two segmented slopes—before and after the change—best explained students' observed change in writing scores. Results also showed that a higher level of initial writing skills was associated with higher levels of intercepts and additional growth gains after the instructional change, whereas the type of instructional change was not associated with predicted writing trajectories. We discuss the implications of positive effects of teachers' individualized timely decisions to change instruction using progress monitoring data as well as unexpected findings and study limitations such as small sample size and inconsistency in results. Educational Impact and Implications Statement: When individualizing instruction for students with the most intensive academic intervention needs, especially in writing, it is recommended that teachers use student progress data to evaluate the effectiveness of instruction. Teachers are encouraged to change their instruction if the data indicates that students are not progressing as expected. We examined whether the instructional changes made by elementary special education teachers while implementing data-based instruction (DBI) improved the early writing outcomes of struggling beginning writers. When teachers made appropriate decisions to change instruction for those students who were experiencing slower-than-expected writing progress within DBI, the students showed facilitated writing growth following these instructional changes. [ABSTRACT FROM AUTHOR]