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.

Educator Perspectives on Data-Based Decision Making in Germany and the United States.

The purpose of this explorative study was to examine the use and understanding of key components of data-based decision making by educators in two countries-Germany and the United States. Educators responded to a survey that asked about data use and characteristics related to data-based decision making (DBDM). Results suggest educators in both countries are focused on using data to monitor progress, although less so in Germany. Educators in both countries noted similar understanding of important features (e.g., psychometric properties) of data. Educators in the United States reported they used data for decision making at the classroom level almost twice as often as their counterparts in Germany, while German educators focused on decision making at the student level. These findings will influence future research, including joint studies that could use the best practices of both countries, and professional learning opportunities for educators in Germany and the United States.

Using latent class analysis to identify academic and behavioral risk status in elementary students.

Identifying classes of children on the basis of academic and behavior risk may have important implications for the allocation of intervention resources within Response to Intervention (RTI) and Multi-Tiered System of Support (MTSS) models. Latent class analysis (LCA) was conducted with a sample of 517 third grade students. Fall screening scores in the areas of reading, mathematics, and behavior were used as indicators of success on an end of year statewide achievement test. Results identified 3 subclasses of children, including a class with minimal academic and behavioral concerns (Tier 1; 32% of the sample), a class at-risk for academic problems and somewhat at-risk for behavior problems (Tier 2; 37% of the sample), and a class with significant academic and behavior problems (Tier 3; 31%). Each class was predictive of end of year performance on the statewide achievement test, with the Tier 1 class performing significantly higher on the test than the Tier 2 class, which in turn scored significantly higher than the Tier 3 class. The results of this study indicated that distinct classes of children can be determined through brief screening measures and are predictive of later academic success. Further implications are discussed for prevention and intervention for students at risk for academic failure and behavior problems. (PsycINFO Database Record