Implementation Drivers of Data-Based Instruction for Students With Intensive Learning Needs: A Systematic Review

Choi, Seohyeon; Shanahan, Emma; Casey-Wilke, Bess; An, Jechun; Johnson, LeAnne

doi:10.1177/00222194231220070

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…Considering that not all students would require instructional changes within the DBM framework, and implementing instructional changes, if not a valid decision, may not ensure improved student outcomes, intensive teacher support might be crucial. While discussing approaches for providing effective teacher support for DBDM is beyond the scope of this discussion, the literature highlights the importance of not only effective PD to enhance teachers’ knowledge and skills but also implementation support at various levels, such as school-embedded support with the involvement of school leaders and administrators (e.g., Choi, Shanahan, Casey-Wilke, et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Longitudinal effects of data-based instructional changes for students with intensive learning needs: A piecewise linear–linear mixed-effects modeling approach.

Choi,

McMaster,

Kohli

et al. 2024

Journal of Educational Psychology

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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 StatementWhen 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.

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Section: Discussionmentioning

confidence: 99%

Longitudinal effects of data-based instructional changes for students with intensive learning needs: A piecewise linear–linear mixed-effects modeling approach.

Choi,

McMaster,

Kohli

et al. 2024

Journal of Educational Psychology

Self Cite

View full text Add to dashboard Cite

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Exploring Predictors of Teachers’ Sustained Use of Data-Based Instruction

An,

Shanahan,

Choi

et al. 2024

J Learn Disabil

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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.

show abstract

Implementation Drivers of Data-Based Instruction for Students With Intensive Learning Needs: A Systematic Review

Cited by 2 publications

References 59 publications

Longitudinal effects of data-based instructional changes for students with intensive learning needs: A piecewise linear–linear mixed-effects modeling approach.

Longitudinal effects of data-based instructional changes for students with intensive learning needs: A piecewise linear–linear mixed-effects modeling approach.

Exploring Predictors of Teachers’ Sustained Use of Data-Based Instruction

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