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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".