What Are Education Students’ Perceptions of the Role of Technology in Social Studies Pedagogy?
Bibliographic record
Abstract
Preservice teachers need both awareness of and skill with the latest digital technologies in order to use these tools effectively in their teaching. Historically in our university, this preparation has been reserved for a stand-alone information technology course focused on learning how to use various computer-based programs. However, more direct experience in subject-specific pedagogy courses is necessary to develop a deeper understanding about how a technology-rich environment can help to develop subject-specific knowledge. The study reported here examined the influences of two technology-infused social studies pedagogy courses on students’ perceptions about why, when, and how most effectively to infuse technology in their teaching of social studies and their feelings of preparedness to use those technologies. Les stagiaires doivent être au courant des technologies numériques et ils doivent savoir s'en servir de sorte à les intégrer efficacement à leur enseignement. Dans notre université, cette préparation a toujours été offerte dans le contexte d'un cours autonome portant sur la technologie de l'information et l'emploi de divers programmes informatiques. Toutefois, il faut avoir plus d'expériences directes dans des cours de pédagogie disciplinaires afin de pouvoir mieux tirer profit d'un milieu riche sur le plan informatique dans le développement de connaissances spécifiques aux disciplines. Cet article décrit une étude portant sur deux cours de pédagogie hautement informatisés et ayant trait aux études sociales. On a examiné, d'une part, les perceptions des étudiants quant à l'intégration efficace de la technologie dans leur enseignement des études sociales (pourquoi, quand et comment) et, d'autre part, la mesure dans laquelle ils se sentaient prêts à employer ces technologies.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".