Professional learning design framework: supporting technology integration in Alberta
Bibliographic record
Abstract
Researchers around the world are interested in knowing how to support teachers in developing both their technology skills and their understanding of how educational technologies can provide opportunity to engage all learners at their skill and interest level in learning activities that were not possible without technology. The solution involves the design and development of teacher professional learning (PL). This study examines a snapshot of one school district, which has experienced a growth in available digital student technology occurring at the same time when teachers experienced a loss of traditional pen and paper resources. Qualitative and quantitative data were gathered and analysed to determine what features of PL would best support teachers in this district. These findings were then considered within the scope of government suggested policy, frameworks and reports. The final suggested framework is for a PL that is collaborative, grade and subject relevant; offers hands-on opportunities; is supported by coaching; is based on research; and is supported by leadership which provides both time and a collaboratively developed vision.Published: 5 February 2018Citation: Research in Learning Technology 2018, 26: 1989 - https://dx.doi.org/10.25304/rlt.v26.1989
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".