Impact of Instructional Role of Supervisors on Students’ Academic Performance in Selected Senior High Schools
Bibliographic record
Abstract
Purpose: Research has shown that instructional supervision is a significant predictor of students’ academic achievement and success in first and tertiary educational systems. One wonders whether this variable will predict the academic performance of senior high schools in developing economies. This study, therefore, aimed at investigating the impact of instructional supervision on students' academic performance in selected Senior High Schools in a developing economy in Sub-Saharan Africa. Methodology: Utilizing a descriptive survey research design, 603 respondents, including staff and students, were surveyed using Instructional Supervision Standards, Procedures, and Tools (ISSPAT) and tests as data collection instruments. Various sampling methods were employed to select participants, including proportionate stratified, purposive, and systematic sampling. Data analysis included techniques such as multiple linear regression, mean, and standard deviation. Findings: The findings indicated that the developmental instructional supervision style emerged as the most significant predictor of students' academic performance in examinations. However, this supervision style was found to be the least utilized by teachers. Unique Contribution to Theory, Policy, and Practice: This study contributes to theory by synthesizing clinical, developmental, and collegial instructional supervision models to create a framework that aligns educational practices with supportive teacher development while improving student outcomes. In terms of policy and practice, the findings underscore the need for educational authorities, particularly the Ghana Education Service, to prioritize and implement a developmental supervision style, which has been identified as the most effective predictor of students' academic performance, thus guiding future instructional supervision strategies and professional development programs for teachers.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".