A multi-centric investigation of technology integration in health professions education: The importance of educators’ Technological Pedagogical and Content Knowledge
Bibliographic record
Abstract
The widespread acknowledgment of Information and Communication Technology (ICT) as a fundamental component of educational strategies is a key context for understanding the findings of our study, which focused on the use of technology by educators in higher education, particularly in the health professions domain. This study examines technology usage patterns and factors influencing active implementation of technology among health professions educators. A cross-sectional survey involving 202 educators from six institutions in Morocco reveals their high confidence in their Technological Pedagogical Content Knowledge (TPACK), notably in Pedagogical and Content domains. The study highlights a prevalence of passive digital learning activities over active approaches. Educators actively employing technology display enhanced TPACK, emphasizing the significance of TPACK development for effective technology integration in teaching practices. Among those with a good level of active technology use, a majority (102 out of 119) have a high TPACK level, and 54 out of 83 of those categorised as low-level participants have a low TPACK level. The findings contribute valuable insights with both theoretical and practical implications for educators in the health professions, emphasizing the significance of aligning technology use with effective instructional practices in this specialized field. Further research should be pursued with diverse methodological approaches to gain a deeper comprehension of the technology integration process in the health profession domain.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".