Technology-Enabled Learning: Policy, Pedagogy and Practice
Bibliographic record
Abstract
Teaching and learning have undergone considerable transformation from the traditional classroom model to the current online and blended models. Developments in information and communications technologies hold the key to such transformation. Seizing the opportunities and affordances of these technologies, COL’s Technology-Enabled Learning (TEL) initiative has focused on several activities to support governments and educational institutions in the Commonwealth since July 2015. // Significant and sustainable interventions include: the Commonwealth Digital Education Leadership Training in Action programme; ICT in education policy development, including open educational resources policy and implementation; massive open online courses on TEL and blended learning practices; systematic TEL implementation in educational institutions; and advanced ICT skills development. // Technology-Enabled Learning: Policy, Pedagogy and Practice, based mostly on various TEL projects in the last five years, presents diverse experiences of TEL from a critical research perspective, offering lessons that can be deployed elsewhere. The book’s 17 chapters provide success stories about the planned and systematic integration of technology in teaching and learning, and present models for online training at scale using massive open online courses and other platforms. Within the framework of the policy–technology–capacity approach to TEL implementation at the micro, meso and macro levels, the chapters also provide guidelines for researching and evaluating similar projects and interventions. // In the post-COVID-19 world of education, the lessons learnt and recommendations in this book will help policy makers and educational leaders rethink existing models of education and training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.036 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".