Bibliographic record
Abstract
The purpose of this paper is to provide some suggestions for consideration of directions for Australian educational research about ICT in education, more recently being called digital learning. A number of agencies in Australia, Canada, Europe, New Zealand, the UK, and the USA have already embarked on developing research agendas and undertaking research into the use of digital learning. This report highlights some of the significant areas in which research has been undertaken and isolates those areas where there may be a need for further research or where there are gaps in the research agenda for Australia. This paper is written as an overview, raising a number of issues relevant to establishing a research agenda for teaching and learning using ICT. However, the development of a national research agenda needs to be the result of discussion and agreement between a number of researchers and experts in the area of ICT in education. This paper is seen a possible starting point for that discussion.
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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 | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".