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Researchers have demonstrated the importance of teacher quality as a lever for improving student achievement (e.g., Hendriks, 2010), including input measures such as teacher knowledge (Goldhaber et al., 2015). In addition, matching effective instructional techniques to individual students’ characteristics is related to increased student achievement (i.e., instructional match; Connor et al., 2004; Shanahan, 2017). Understanding the complex relationship between teacher knowledge and instructional match can provide important guidance for both teacher preparation and classroom instruction. However, research has yet to examine how these factors influence literacy development for young English learners (ELs), a group of students who experience difficulty learning to reading in English when compared to their native English-speaking peers (NCES, 2018). To address this gap, we explored the following question: What is the relationship between young English learners’ literacy development and teachers’ knowledge of reading content and instructional match?
Participants included 78 teachers (29 kindergarten, 49 first grade) and 234 children (89 kindergarten, 145 first). Teacher knowledge was measured in two ways. First, reading content knowledge was measured using the Teacher Knowledge Assessment (TKA; Piasta et al., 2009), a 34-item adaptation of four existing reading knowledge measures (Bos et al., 2001; Mather, et al., 2001; Moats, 1994; Moats & Foorman, 2003). Second, instructional match (IM) was measured with a researcher-designed series of vignettes and corresponding Likert-scaled questions. Students’ literacy achievement was measured in the fall and spring using the Measures of Academic Progress for Primary Grades reading test (MAP-PG; NWEA, 2014), a computer adaptive measure of general literacy achievement.
Given the nested structure of the data, a two-level (children in classrooms) random effects model was used to examine the extent to which the TKA and IM predicted literacy achievement and growth for young ELs. The average cluster size was approximately four ELs per teacher (range 1-9). First, a level-one model estimated MAP-PG in the spring, predicted by MAP-PG in fall. In the level-one model, fall MAP-PG was a strong and significant predictor (β = 0.76, p < .001). Next, we added the TKA and IM in a level-two equation, where these variables predicted spring MAP-PG, and were allowed to correlate with fall MAP-PG, and with each other.
Table 1 displays the results from the two-level model. Overall, the model explained 55% of the variance at level-one and 54% and the variance at level-two. Fall MAP-PG remained a significant predictor of spring MAP-PG, β = 0.706, p < .001. Most notably, the TKA was significantly related to spring MAP-PG, β = 0.723, p < .00, after controlling for fall reading achievement. Additionally, the TKA was significantly correlated with fall MAP-PG (β = 0.482, p < .001). However, IM was not significantly related to MAP-PG in either fall or spring, but was significantly related to the TKA (β = 0.535, p < .001).
Results suggest that the TKA is a robust predictor of achievement and growth in literacy achievement for young English learners. Additionally, there appears to be considerable overlap between measures of teacher knowledge and instructional match.
Steve Amendum, University of Delaware
Presenting Author
Adrian Pasquarella, University of Delaware
Non-Presenting Author
Austin Jennings, University of Delaware
Non-Presenting Author
Annastasia Purinton, University of Delaware
Non-Presenting Author
Mary Bratsch-Hines, UNC-Chapel Hill
Non-Presenting Author
Lynne Vernon-Feagans, University of North Carolina at Chapel Hill
Non-Presenting Author
Cheryl Varghese, University of North Carolina at Chapel Hill
Non-Presenting Author
Leslie Babinski, Duke University
Non-Presenting Author
Horatio Blackman, University of Delaware
Non-Presenting Author