Proceedings of the first ACM international workshop on Multimedia technologies for distance learning
Bibliographic record
Abstract
The organizing committee of the Second ACM International Workshop on Multimedia Technologies for Distance Leaning (MTDL 2010) will like to welcome all of you to join the workshop as well as the ACM Multimedia Conference 2010. Education is considered as one of the most important social activities by all countries and governments, especially in the age of information exchange through the Internet. Distance learning technologies, although still rely on educational professionals to have guidance, computer and communication technologies help remove the time and space restrictions that exist in traditional learning. This MTDL workshop aims to discuss new contributions as well as practical experiences using computer and communication technologies. Especially, it looks at the differences of using and without using multimedia technologies for education. In general, multimedia technologies can help students learn in a more interesting way as compared to traditional education methods, with sound and various media. Some educational theories also suggest that a few special trainings can be constructed much efficiently with the help from interactive multimedia. For instance, interactive simulation is useful in medical training. Additional technologies to fuse multidimensional information for e-learning are also interesting contributions from computer and sociological perspectives. On the other hand, interesting learning materials rely on good authoring technologies to combine learning activities for students. The materials for supporting these activities need to be located from local or a remote database and be retrieved efficiently. Therefore, how to search and rank learning objects from distance learning repositories has become not only a computer technology-based problem but also a communication issue. Communication technologies can also improve the attractiveness of social games and game-based learning. This year, we received a total of 14 papers (12 papers submitted and 2 papers recommended by the main conference), the program committee decided to accept 6 of them that were related to the above issues. These papers are from Canada, Hong Kong, Italy, Japan, Spain, Taiwan, and United Kingdom. Each paper was reviewed by at least three program committee members and discussed by the program committee co-chairs before acceptance. The overall acceptance rate is less than 43%.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.068 | 0.027 |
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".