Culturally Responsive Leadership: Fostering a Shared Vision of Culturally Responsive Teaching for Learners of English as an Additional Language
Bibliographic record
Abstract
Schools in Alberta are becoming increasingly diverse, leaving teachers feeling unprepared with how to best support students. Given the persistence of these challenges and the increasing population of students who are English as an Additional Language (EAL) the aim of this study was to determine ways in which leaders can lead a culturally responsive school. To begin to support this diverse group of students, leaders must lead culturally responsive schools to meet the needs of all learners and help build a sense of belonging for students. To achieve this, researchers have identified that educational leaders must support their teachers \nwith professional learning to implement and sustain culturally responsive teaching practices and promote a culturally responsive school culture. This capstone study provides a review of the literature regarding what Culturally Responsive Teaching (CRT) is and how it both impacts students’ learning and helps build a sense of belonging for students. From a leadership perspective, this capstone study focuses on how leaders can foster a shared vision for culturally responsive schools and what their role is in supporting teachers in self-reflection and professional development in CRT. Implementation of these strategies promote the academic achievement of EAL students by helping them feel engaged and motivated to learn. Following the literature review, recommendations are provided to suggest strategies school leaders can implement to promote a culturally responsive school culture that helps bridge the gap for EAL learners. \nKeywords: educational leadership, culturally responsive leadership, culturally responsive teaching, culturally responsive schools, student achievement, ELL, ESL, unconscious bias, sense of belonging, shared vision, professional development, change agents, collaborative learning, trust
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".