Teacher-Student Relationships and High School Drop-out: Applying a Working Alliance Framework
Bibliographic record
Abstract
Relationships with teachers are a central component of a student’s school environment, and have been shown to be related to school engagement and persistence in secondary school. Working alliance is a conceptualization of professional relationships that emphasizes not only the emotional bond between a professional and their client, but also their collaboration on the goals and tasks of their work together. While this theory has garnered considerable support in the fields of counseling and healthcare, working alliance has only recently begun to be investigated in an education setting. The present study sought to investigate working alliance between students and teachers as a broader framework for relationships in a high school setting. Specifically, the primary objective was to examine the use of the working alliance framework in teacher-student relationships to predict risk of high school student drop-out. A series of multiple regressions was used to test this objective. Results demonstrated that student-rated school working alliance predicted risk of drop-out, and that the relationship was partially mediated by student engagement. These results provide evidence for the validity of the construct of working alliance as a useful conceptualization for teacher-student relationships, and enhance our understanding of working alliance in a secondary school setting. Implications for educators and practitioners are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".