Teaching Pre-Service Teachers to Integrate Serious Games in the Primary Education Curriculum
Bibliographic record
Abstract
Curriculum integration is one of the main factors in the teachers’ decision-making process when deciding to use games in formal educational contexts. Based on this observation, we aim to introduce pre-service teachers to Game Based Learning (GBL) and Serious Games (SG) integration in the curriculum. The teaching experience aims to facilitate different approaches to GBL and SG integration in the curriculum, including three types of GBL activities. Firstly, the use of Serious Games (SG), designed for educational purposes from the start; secondly, the game creation as a learning activity through game authoring platforms; thirdly, the use of repurposed entertainment games, which, despite not having being intentionally designed for educational purposes, could be diverted for meeting the curriculum objectives of primary education. A group of 51 pre-service teachers participated in the teaching experience during which they selected a GBL activity among the three types of GBL and SG integration in the curriculum. Most of the teachers succeed to identify SG created for educational purposes, and we observed 6 entertainment games repurposed for educational objectives, none of the pre-service teachers decided to integrate a game creation activity in the curriculum. We analyze the results of the teaching pre-service experience and the opportunities to introduce GBL and SG in pre-service teachers’ education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".