eLearning and Initial Teacher Education Programs: Insights from the Teaching Teachers for the Future Project
Bibliographic record
Abstract
This paper argues that initial teacher education programs (ITE) which build the Technological Pedagogical Content Knowledge (TPACK) (Mishra & Koehler 2006) confidence and capabilities of future teachers is critical in enabling effective design and implementation of eLearning for school students. Insights are provided through drawing upon selected research and evaluation findings from the Teaching Teachers for the Future (TTF) Project involving all HEIs which provide ITE programs in Australia. The TTF Project, a 15 month long, $8 million project was funded by the Australian Government's ICT Innovation Fund and aimed to develop the ICT capabilities of future teachers. Findings from the TTF Project indicate that the TPACK conceptualisation used to guide the project, and the Australian Institute for Teaching and School Leadership's ICT Elaborations for Graduate Teacher Standards (AITSL 2011a) can inform the design of ITE programs in preparing future teachers for using ICT to support teaching and to support student learning.
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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 | 0.008 | 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 | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".