Pre-Service Teachers’ Perspectives on Learning to Teach Social Studies in a Technology-Rich Pedagogy Course
Bibliographic record
Abstract
Preparing new teachers for teaching with technology is a multi-faceted process. The study reported in this chapter examined the impact that immersion in two technology-enriched, pre-service social studies pedagogy courses had on the way beginning teachers approached technology use in their teaching of social studies. The study took place over two years and tracked education students through their social studies pedagogy course experiences and their practice teaching as part of their teacher preparation program then into their first year of teaching. The findings identified that the pre-service pedagogy courses did assist in increasing the education students’ understanding of a variety of ways to approach the use of various technology tools as well as their willingness to use them in their teaching. However, the results also point to the importance of pre-service teachers’ developing a positive attitude and a willingness to take risks with technology; of all instructors being prepared to infuse technology use in their classes in ways that fit with what is current in the schools; and of schools and mentor teachers encouraging, supporting, and modeling best practice with technology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".