Teaching as Inquiry: Teachers Understanding and its Implication for Teaching and Learning
Bibliographic record
Abstract
Teaching as inquiry (TAI) is described as a component of effective pedagogy that has significant impact on learning outcome. Based on this premises, the study sought to examine TAI and its implications for teaching and learning in the Cape Coast Metropolis, Ghana. The descriptive survey design was adopted for the study and a sample of 160 basic school teachers was selected from three circuits within the Cape Coast Metropolis. A questionnaire and an interview guide were used in collecting the data for the study. Data obtained from the questionnaire were analyzed using descriptive statistics (frequency count, percentages and means and standard deviations) whereas the interview data was transcribed and presented by doing thematic analysis. The study revealed that majority of the respondents had knowledge about TAI. In addition, the study showed that the teachers engaged in TAI practices such as reflecting and questioning their methods of teaching, identifying the academic needs of students before planning instruction and engaging in projects and research concerning the subject and content to be delivered. Findings from the study showed that teachers who participated in the study have embraced the concept of TAI very well and considering how they can mentor and lead others. It was recommended that the Cape Coast Metropolitan education office should organize intermittent workshops and training for teachers on TAI to help teachers build specific skills and refinements for reflection and action and planning strategies to support learners to learn specific things (content or skills) that teachers can specifically monitor in terms of student outcomes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".