Shifting Views: Exploring the Potential for Technology Integration in Early Childhood Education Programs / Changement d’opinion: Exploration du potentiel d’intégration de la technologie dans les programmes d’éducation de la petite enfance
Bibliographic record
Abstract
Using technology with children in play-based early learning programs creates questions for some within the Early Childhood Education (ECE) community. This paper presents how two faculty who teach in ECE-related degree programs integrated educational technology into their teaching pedagogy as a way to model to their students how it can be used to support children’s play and learning opportunities. The authors identify how collegial dialogue helped them to use various technologies and social media in their teaching, which transformed their curriculum and pedagogical philosophy. The paper argues that if technology creates connections between learning in the college or university classroom and is effective practice, it is worthy of further exploration. L’utilisation de la technologie avec des enfants dans des programmes préscolaires d’apprentissage basé sur le jeu suscite des questions pour plusieurs au sein de la communauté de l’Éducation de la petite enfance (EPE). Cet article présente la façon dont deux professeurs enseignant dans des programmes d’études liés à l’EPE ont intégré la technologie éducative dans leur pédagogie d’enseignement comme un moyen de démontrer à leurs étudiants comment elle peut être utilisée pour soutenir le jeu des enfants et les possibilités d’apprentissage. Les auteurs montrent comment un dialogue collégial les a aidés à utiliser diverses technologies et médias sociaux dans leur enseignement, ce qui a eu pour effet de transformer leur programme et leur philosophie pédagogiques. L’article fait valoir que si la technologie crée des liens entre l’apprentissage en milieu postsecondaire et une pratique efficace, elle est par conséquent digne d’une exploration plus poussée.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".