A Reflective Approach to Digital Technology Implementation in Language Teaching: Expanding Pedagogical Capacity by Rethinking Substitution, Augmentation, Modification, and Redefinition
Bibliographic record
Abstract
As the number of language instructors seeking to implement digital technologies in their teaching continues to grow, so does the need for direction with regard to making pedagogically sound decisions concerning digital tool use. One popular and useful guide for considering the educational potential of digital technologies has been Puentedura’s (2006) Substitution-Augmentation-Modification-Redefinition (SAMR) model, with its four levels of progressive technological integration. However, the degree of technological integration truly possible or even desirable for individual teachers in their given context depends on a number of complex, interrelated, largely non-technological factors, including implementation motives, pedagogical purview, educational philosophy, theory of learning, teaching style, and situational constraints. Generally unconscious, these factors often go ignored, leaving teachers susceptible to technological decisions that can lead them to lose their prescribed pedagogical focus or unwittingly contradict their core professional beliefs. After a brief, situated overview of the SAMR model, this article introduces and illustrates a five-stage SAMR-embedded reflective approach to systematically eliminating irrelevant, unacceptable, and unfeasible instructional uses of technology and, thereby, revealing potential for expanding pedagogical capacity in language teaching. À mesure que grandit le nombre de professeurs de langue qui cherchent à mettre les technologies numériques au service de leur enseignement, il devient plus important de savoir prendre des décisions pédagogiques judicieuses concernant le recours aux outils numériques. Populaire et utile avec ses quatre niveaux d’intégration progressive de la technologie, le modèle SAMR (Substitution, Augmentation, Modification, Redéfinition) de Puentedura (2006) a guidé maints utilisateurs intéressés par le potentiel éducatif des technologies numériques. Toutefois, le degré d’intégration technologique effectivement possible ou même désirable pour les professeurs individuels dans leur contexte particulier dépend de facteurs complexes, interdépendants et essentiellement non technologiques tels que les motifs invoqués en faveur du recours à la technologie, le ressort en matière de pédagogie, la philosophie éducative, la théorie de l’apprentissage, le style pédagogique et les contraintes situationnelles. Généralement inconscients, ces facteurs restent souvent ignorés, ce qui risque de confronter les professeurs à des décisions technologiques susceptibles de leur faire perdre la focalisation pédagogique qui leur a été prescrite ou de contredire involontairement leurs convictions professionnelles fondamentales. Après avoir brièvement replacé le modèle SAMR dans son contexte, le présent article introduit et illustre une approche réflective en cinq étapes intégrées au modèle SAMR qui est destinée à éliminer systématiquement les utilisations non pertinentes, inacceptables et irréalisables de la technologie, et ouvrant ainsi la perspective d’enrichir le potentiel pédagogique de l’enseignement des langues.
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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 | 0.052 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.067 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".