Unpacking the Information, Media, and Technology Skills Domain of the New Learning Paradigm
Bibliographic record
Abstract
Put simply, “Teaching our students so that they become well-equipped with the 21st century skills is the new learning paradigm” (Kivunja, 2014b, p. 85). These skills fall into four domains which the Partnership for 21st Century Skills (P21) identify as the Traditional Core Skills, the Learning and Innovation Skills, the Career and Life Skills, and the Digital Literacy Skills; also known as the Information, Media, and Technology Skills (P21, 2009). Arguing that the traditional core skills, such as reading, -riting, and –rithmetic are well known, and might need no elaboration, Kivunja (2014b) discussed the Learning and Innovations Skills domain, and Kivunja (2015a) unpacked the Career and Life Skills domain. This paper unpacks the Digital Literacy Skills domain to extend an understanding of this domain in three ways. First, what is it and what skills does it involve? Second, how can students be taught the skills of this domain so they will be job ready to use these skills on graduation? Third, what is the significance of this domain to each of the other domains; and therefore to the success of studying, working, living and being a productive citizen in the realities of the Digital Economy?
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".