Measuring the Impact of Technological Pedagogical Content Knowledge on Teacher Resilience in Universities of Pakistan
Bibliographic record
Abstract
The aim of this Empirical Paper is to determine the impact & linkage of Technological Pedagogical Content Knowledge with Teacher Resilience among teachers of Pakistan. Data collection was conducted in 7 universities, 5 Colleges, 3 Schools and 2 Academies including public and private educational institutes of 3 cities of Pakistan by using simple random sampling technique. Self-administered questionnaires were distributed among 425 teachers. With 92.94% response rate, 395 questionnaires were responded positively. 377 responses were found useable. Confirmatory Factor Analysis, Reliability Analysis, Frequency Distribution Analysis, Pearson's R correlation & Multiple linear Regressions analysis techniques were used to analyze the data on SPSS PSAW version 22.The Regression model is moderately parsimonious with 52.7% of the variance. TPACK Framework on the whole have positive impact (β=.439) & have positive strong significant relationship (.702**) at the 0.002 level of significance with teacher resilience. Technology Knowledge has positive impact (β=.478) & have positive moderate significant relationship (.461**),Pedagogy Knowledge has positive impact (β=.512) & have positive moderate significant relationship (.573**),Content Knowledge has positive impact (β=.412) & have positive moderate significant relationship (.398**),Pedagogical Content Knowledge has positive impact (β=.401) & has positive moderate significant relationship (.429**), Technological Pedagogical Knowledge has positive impact (β=.295) & have positive moderate significant relationship (.322**),Technological Content Knowledge has positive impact (β=.478) & have positive moderate significant relationship (.418**),Technological Pedagogical Content Knowledge has positive impact (β=.307) & have positive moderate significant relationship (.497**) & Context Knowledge has positive impact (β=.395) & has positive moderate significant relationship (.330**) with teacher resilience. This study is significant enough to support the teachers and educational policy makers to adopt technology based pedagogical approaches to foster resilience among teachers and educational settings. By using cross sectional research design, the study was conducted in context of Pakistan. The model can be studied by scholars in future by using longitudinal & time series research design to increase generalizability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".