Exploring the Effects of Culturally Responsive Instruction on Reading Comprehension, Language Comprehension, and Decoding with Bayesian Multilevel Models
Bibliographic record
Abstract
Reading comprehension (RC) can be predicted from language comprehension (LC) and decoding, and all three constructs are responsive to structured teaching. Culturally responsive instruction, which explicitly connects students’ lived experiences with school experiences, can also effectively support literacy learning. However, little is known about how structured and culturally responsive approaches work in tandem, and whether positive effects may occur through the path of LC or decoding, or directly on RC. Further, does culturally responsive teaching support transfer from local, personalized learning materials to standardized measures? This study investigates the impact of structured and culturally responsive teaching on standardized measures of RC, LC, and decoding among 263 students in grades 1 through 3. Participants were assigned to one of three groups: (1) generic structured teaching approach that used mainstream materials, (2) a structured culturally responsive approach that centered students’ interests, cultures, and sense of belonging, and (3) a waitlisted business-as-usual control group. Over 10 weeks, students received small-group teaching focused on decoding and LC. Bayesian multilevel ANCOVA models indicate all groups grew, with differential positive effects for LC for the culturally responsive treatment group. The findings suggest benefits to integrating cultural relevance into structured literacy teaching and that a multifaceted approach may be effective. Implications and limitations are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".