Fostering 4.0 Digital Literacy Skills Through Attributes of Openness: A Review
Bibliographic record
Abstract
During the last decade, a growing interest in open educational resources (OER) has developed among educational researchers worldwide. This trend involves the examination of possible effects over diverse learning domains such as the development of literacy and digital skills in the context of the fourth industrial revolution. To address this matter, a systematic literature review was conducted using PRISMA processes on 62 research articles published in high-impact peer-reviewed journals indexed in two major academic databases (Scielo and Scopus). Data collected during this literature review showed certain conditions that must be met to ensure a successful learning setup when OER are involved. Moreover, qualitative analysis revealed that certain attributes of openness are often more influential than others in the development of adequate literacy skills for the artificial intelligence era; also, there is an overall positive perception, from students and teachers alike, about the introduction of the attributes of openness and open materials into learning practices.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.010 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".