E-learning in school education in the coming 10 years for developing 21st century skills: Critical research issues and policy implications
Bibliographic record
Abstract
One of the curriculum goals of e-learning in school education is to develop learners for 21st century skills through their daily learning activities. This paper aims to discuss the research issues and policy implications critical for achieving such a curriculum goal. A review of literature in the related fields indicates that K-12 schools should take advantage of e-learning to maximize learning opportunities of learners for the development of 21st century skills. We identify six research issues critical for e-learning in school education, namely the realization of developing 21st century skills of learners; the bridging of the gap between curriculum in school and situations in society; the maximization of learning opportunities in the learning process; the collection of evidence of improvement and building awareness of progress; the assessment of 21st century skills; and the provision of teacher development for enculturating learners to develop 21st century skills. We recommend the relevant stakeholders across different countries/regions to consider policies on the goal-setting of curriculum addressing 21st century skills development and bridging gap between school and society; on the availability of digital technology for school education; on the privacy/legal issues of learning data in e-learning process; and on the teacher development for pre-service and in-service teachers.
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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 | 0.043 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".