A Case Study of Infusing Technology into Pre-service Secondary Science Teacher Learning: Conceptions and Attitudes While Navigating Changing Digital Landscapes
Bibliographic record
Abstract
The impact of technology on student learning, achievement, motivation, and engagement is well documented; however, little research exists on how educators navigate their changing roles within a technologically enabled classroom. Authors explored how effective and appropriate use of classroom technology based on pre-service teachers’ needs and connected to STSE and STEM curriculum can be modeled. This research focused on the preparation of pre-service teachers (n=48) for Grade 6–12 Science teaching in Canada at a small undergraduate university and included how their course professor infused more knowledge of, in, and for practice within the science classroom. Results indicate a need for critical awareness of the processes of learning in 21st century classrooms, an understanding of how technology can enhance students’ achievement, and a value of invested time and effort into the process. Discussion surrounding challenges and tensions, and suggestions to support emerging pedagogical stances of 21st century educators are provided.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".