Bibliographic record
Abstract
Recently many researchers have begun to focus on ubiquitous learning(u-learning) and made some attempts from practical level.However what is ubiquitous learning? How to make the concept of u-learning become a reality? These questions are all worthy of our attention.Nowadays with the development of economy,society,information and communication technology,many countries in the world are paying more attention to lifelong education.Ulearning is a very good choice.In this issue,we interviewed Prof Kinshuk from Athabasca University to have an overall view on u-learning. Dr.Kinshuk is Associate Dean of Faculty of Science and Technology,and Full Professor in the School of Computing and Information Systems at Athabasca University,Canada.He also holds the NSERC/iCORE/Xerox/Markin Industrial Research Chair for Adaptivity and Personalization in Informatics,funded by the federal and provincial governments of Canada and by industries.He has a PhD from De Montfort University,United Kingdom.His work has been dedicated to advancing research on the innovative paradigms,architectures and implementations of online and distance learning systems for individualized and adaptive learning in increasingly global environments.Areas of his research interests include learning technologies,mobile,ubiquitous,location and context aware learning systems,cognitive profiling and interactive technologies.With more than 300 research publications in refereed journals,international refereed conferences and book chapters,he is frequently invited as keynote or principal speaker in international conferences and visiting professor around the world.He was awarded the prestigious fellowship of Japan Society for the Promotion of Science in 2008.He has also been invited as guest editor of 12 special issues of international journals in the past five years,and continues to serve on a large number of editorial boards of prestigious journals and program committees of international conferences.He has also served on review panels for grants for the governmental funding agencies of various countries and regions,including the European Commission,Austria,Canada,Hong Kong,Israel, Italy,the Netherlands,Qatar,Taiwan and the United States.He also has a successful record of procuring external funding over 11 million Canadian dollars as principal and co-principal investigator.In his on-going professional activities, he is Founding Chair of IEEE Technical Committee on Learning Technologies,and Founding Editor of the Educational Technology Society Journal(SSCI indexed with Impact Factor of 1.066 according to Thomson Scientific 2010 Journal Citations Report).At the national level,he is Founding Chair of the New Zealand Chapter of ACM SIG on Computer-Human Interaction,and Past President of the Distance Education Association of New Zealand.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".