Teacher educational qualifications and the quality of teacher–student interactions in senior high school classrooms in Ghana: Could teacher self‐efficacy bridge the qualifications gap?
Bibliographic record
Abstract
Abstract Teacher qualifications, self‐efficacy, and quality of teacher–student interactions (QTSIs) are critical factors in educational discourse. While research shows varied results for each variable, studies have yet to examine all three variables simultaneously. To what extent does teacher self‐efficacy contribute to bridging the qualifications gap toward QTSIs? This study investigates the relationship between teacher educational qualifications and QTSIs and the potential role of teacher self‐efficacy in addressing the qualifications gap. An empirical analysis of 419 valid responses from senior high school (SHS) teachers in Ghana utilizing the t‐test and ordinary least squares estimators uncovered noteworthy findings. The study revealed a positive influence of teacher educational qualifications on QTSIs, with higher qualifications (master's degree) significantly enhancing QTSIs compared to lower qualifications (bachelor's degree). Teacher self‐efficacy positively moderated the impact of teacher educational qualifications on QTSIs. The study also revealed that while higher teacher self‐efficacy was beneficial in bridging the educational qualifications gap between bachelor's and master's degrees on QTSIs, it only partially bridged the gap. This study's findings invite policymakers, teacher educators, and school authorities to employ a balanced approach to improving QTSIs in SHS classrooms in Ghana by encouraging teachers to advance their qualifications and creating an enabling environment to develop their self‐efficacy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".