A formative analysis of how preservice teachers learn to use technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A comprehensive, formal comparison of strategies used by preservice teachers to learn how to use new technology has yet to be researched. Understanding the relative strengths and weakness of learning strategies would provide useful guidance to educators and students. The purpose of the current study was to explore the effectiveness of four learning strategies: collaboration, using authentic tasks, formal instruction and exploratory learning. Seventy‐four preservice teachers (25 male, 49 female) were surveyed at the beginning and end of an 8‐month, consecutive, Bachelor of Education programme, with respect to their learning strategies, change in computer knowledge and use of computers in the classroom. Collaborative learning and use of authentic tasks were the most preferred strategies – formal instruction was the least preferred. A collaborative approach to learning was the best predictor of gains in computer knowledge. Authentic tasks and collaborative strategies were significant predictors of teacher use of computers in the classroom. Preference for authentic tasks was the only predictor of student use of computers. Regardless of strategy preference, selecting more than one primary learning tool was significantly correlated with amount learned and use of the computers in the classroom. Ability was not related to strategy preference. Finally, females preferred collaborative approach to learning, although they were significantly more open to using multiple strategies than males.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it