Algorithmic futures: an analysis of teacher professional digital competence frameworks through an algorithm literacy lens
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.001 | 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