Educating Nonlinearly and Visually in the Digital Knowledge Age: A Delphi Study
Bibliographic record
Abstract
This longitudinal, qualitative and Delphi technique-designed research study sought a reasonable consensus for identifying and prioritizing the best and most appropriate Web 2.0/3.0 nonlinear visual tools that can revolutionize the educational processes of thinking, teaching, learning, and leading in the digital knowledge age. The study also sought reasonable agreement on the criteria to measure the educational success of these new breeds of information and communication technology (ICT) tools. The results were very promising. The Delphi ICT experts, 80 personnel in total, reached significant agreement on the issues that were carefully deliberated in this study. The Delphi participants prioritized a list of the most relevant, reliable, and appropriate Web 2.0/3.0 nonlinear visual tools that can enhance the educational processes of thinking, teaching, learning, and leading in the digital knowledge age. Additionally, the participants recognized and prioritized certain criteria (i.e., measurements, standards, factors, benchmarks, and principles) that can assess and measure the success of Web 2.0/3.0 nonlinear visual tools and any ICT platform from educational perspectives. No significant differences were found among the Delphi subgroups when inferential statistics were performed. The qualitative nature of the Delphi technique research design, the deployment period, and the capability of examining a large sample size of ICT experts all notably helped in strengthening the statistical significance, reliability, and confidence in the collected data. The findings of this study comply with the postulated assumptions. These results can assist academics, educators, instructional technology leaders, practitioners, administrators, and policy and decision makers in ascertaining and defining appropriate solutions to educational challenges.
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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 | 0.067 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".