A Strategic Partnership to Understand the Ecosystem, Adaptability and Transfer of Digital Skills - a Focus on the Educational System
Bibliographic record
Abstract
This paper is a report on the findings of a study on the development of digital skills. Discourse analysis techniques were used to examine the resulting transcript of interviews with experts in technopedagogy for evidence of digital skill development from school and postsecondary contexts. Using the DELPHI method, six experts were consulted about key digital competence. Qualities such as resilience, adaptability and open-mindedness were identified as key. Findings also indicate that digital skill can be defined by one's ability to use technologies and adapt positively to challenges during use. From an educational perspective, our results show that the digital skills needed to succeed in a technological world are not necessarily the ones developed in schools and colleges. For instance, experts agree that educational institutions looking to foster digital skills should move beyond teaching mainly technical ability, focusing instead on developing more analytical or critical ability.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".