An analysis of virtual simulations from the TPACK perspective
Bibliographic record
Abstract
INTRODUCTION. Virtual simulations (VS) have increased their presence in the higher education training actions during the last years and has been consolidated as a result of the COVID-19 pandemic as a powerful tool that allow us to overcome many of the limitations of the face to face simulation rooms, related to costs and replicability. However, there is a lack of studies about the use of theoretical models, such as the TPACK, for the analysis of VS. METHOD. In this article a systematic review of the literature is conducted with the main aim of analysing the characteristics of the VS used in higher education during the last decade (2012-2022) from the optic of the TPACK model. RESULTS. The main findings are the big use of VS in Health-related areas and especially in the American continent (Unite States and Canada); the screen-based and computer-based simulation played online as the most common technological features; and the Experiential learning, the Situated learning and the Problem-based learning as the most common theories for the pedagogical justification of VS in higher education. DISCUSSION. A series of relationships has been spotted among the technological, pedagogical and content features of the VS that help us to better understand this tool that has a growing use, especially in the health field. Conclusions show, on the one hand, the lack of articles that properly describe the use of VS according to the TPACK requirements and, on the other hand, the adequacy and viability of this model for the analysis and development of VS.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".