Algorithmic futures: an analysis of teacher professional digital competence frameworks through an algorithm literacy lens
Bibliographic record
Abstract
Algorithmic systems shape every aspect of our daily lives and impact our perceptions of the world. The ubiquity and profound impact of algorithms mean that algorithm literacy—awareness and knowledge of algorithm use, and the ability to evaluate algorithms critically and exercise agency when engaging with algorithmic systems—is a vital competence for navigating life in the 21st century. Professional digital competence (PDC) frameworks for teachers include technological, pedagogical, and social competence areas and are intended to illustrate the necessary knowledge, skills, and attitudes for digitally competent teachers. Using document analysis, we undertook a systematised review and evaluation of selected PDC frameworks through the lens of algorithm literacy. This analysis demonstrated that although some aspects of algorithm literacy could be inferred within the PDC frameworks analysed, there is a need for further explicit integration. Just as the DigComp framework for citizens has been updated to recognise the vital importance of understanding algorithmic systems' impact, so should PDC frameworks be revised. Recommendations are provided for incorporating understandings of algorithmic governance and bias and ensuring digital Bildung development in PDC frameworks. Implications for teacher education programmes are also discussed.
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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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".