Conceptions and Instructional Strategies of Pre-Service Teachers towards Digital Game based Learning Integration in the Primary Education Curriculum
Bibliographic record
Abstract
Teachers' digital literacy is part of the 21st century professional competences and is an essential part of the decision-making process leading to integrate the use of technologies in the classroom according to the curricular needs. This article focus on the teachers' competence to integrate technologies in the classroom by analyzing their integration strategies. The teachers' curricular integration strategies are analyzed in this article by analyzing Digital Game Based Learning (DGBL) curricular integration strategies with a group of 73 pre-service primary teachers in Université Laval (Canada). The results show the pre-service teachers selected the use of existing resources instead of the creation of new ones. The majority of the selected resources were games in the are of Mathematics. The participants discussed this strategy as the easiest way to align the digital games in the primary education curriculum. The authors discuss, at the end of the paper, the limits of this strategy and the opportunities to develop alternative ways to integrate digital games in the classroom to develop the curricular objectives such game repurposing and the creation of digital games as a learning activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".