Teacher Predictors of Student Progress in Data-Based Writing Instruction: Knowledge, Skills, Beliefs, and Instructional Fidelity.

Teacher-level factors are theoretically linked to student outcomes in data-based instruction (DBI; Lembke et al., 2018). Professional development and ongoing support can increase teachers' knowledge, skills, and beliefs related to DBI, as well as their instructional fidelity (McMaster et al., 2020). However, less is known about how each of these teacher-level factors influences student progress during an intervention. The purpose of this study was to examine the association between several important teacher-level factors-teachers' writing instruction fidelity, knowledge and skills related to DBI, explicit writing orientation, and writing instruction self-efficacy-and students' writing growth. Participants were 49 U.S. elementary teachers and their 118 students struggling with early writing skills. Using hierarchical linear modeling, we found a significant positive relation between DBI knowledge and skills and student writing growth, but no relation was found between writing instruction fidelity, writing orientation, or self-efficacy and student writing growth. Implications for writing instruction fidelity measurement in DBI and professional development related to teachers' DBI knowledge and skills are discussed.

Exploring the Relation Between Teacher Factors and Student Growth in Early Writing.

Data from a small randomized control trial of teachers' use of Data-Based Instruction (DBI) for early writing were analyzed to determine the influence of teacher knowledge, skills, and treatment fidelity on student Curriculum-Based Measurement (CBM) slope. Participants included 11 elementary grade teachers who delivered intensive intervention in early writing and their students (n = 31), all identified as either at risk for or with disabilities that affect their writing. Teachers received professional development and ongoing coaching to support the implementation of DBI for improving their students' early writing skills. Results from a multiple regression analysis suggest that teacher knowledge and skills in DBI was strongly related to student CBM slope in early writing (p < .01) and a small but significant relation between fidelity of writing instruction and student CBM slope (p < .01). Implications for instructional coaching and improving student writing progress are discussed.

The Inferential Language Comprehension (iLC) Framework: Supporting Children's Comprehension of Visual Narratives.

We present an integrated theoretical framework guiding the use of visual narratives in educational settings. We focus specifically on the use of static and dynamic visual narratives to teach and assess inference skills in young children and discuss evidence to support the efficacy of this approach. In doing so, first we review the basis of the integrated framework, which builds on major findings of cognitive, developmental, and language research highlighting that (a) inference skills can be developed in non-reading contexts using different media, (b) inference skills can transfer across different media, and (c) inference skills can be improved using questioning that includes scaffolding and specific feedback. Second, we review instructional and assessment approaches that align with the proposed framework; these approaches are designed to teach or assess inference making skills using visual narratives and interactive questioning. In this context, we discuss how these approaches leverage the unique affordances of static and dynamic visual narratives with respect to unit of meaning (by increasing opportunities to generate inferences), multimodality (by providing opportunities to generate inferences of higher complexity than text), and vocabulary/knowledge demands (by providing vocabulary/knowledge support), while also reviewing evidence for their usability, feasibility, and efficacy to improve educational outcomes. We conclude with important theoretical and practical questions about future work in this area.