PREFACE Technology for learning: how it has changed education
Bibliographic record
Abstract
The Author(s) 2014. This article is published with open access at Springerlink.com As a result of the digital revolution we have experienced in the last 25 years, a range of new teaching and learning formats is available, from e-modules, sophisticated simulations and serious games, to online collaborative learning. In this special issue, we cover several of these promising new formats of technology-enhanced learning (or e-learning or online learning), referring to the use of internet technologies to deliver a broad range of solutions that enhance knowledge and performance [1, 2]. For health care and medical education, with its growing demands on physicians competencies and decreasing supply of hospital-based patients [3], flexible, scalable and engaging learning opportunities are essential to meet the new demands. Traditional models of classroom-based learning as dominant training model no longer meet the current needs of health care institutions [4]. The role of technology-enhanced learning in health education has grown rapidly; over 90 % of medical schools in the USA and Canada use online course materials for medical education [5]. Although some people state that because of the technological change ‘today’s students are no longer the people our educational system was designed to teach ’ [6, p. 1], there is little evidence that students enter university with demands for new technologies that teachers cannot meet [7]. Selection and use of formats (such as e-modules or simulations) in technology-enhanced learning should be based on informed choices of effectiveness and costs, with instructional objectives being in the lead.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.061 | 0.028 |
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".