Proceedings of the international workshop on Educational multimedia and multimedia education
Bibliographic record
Abstract
Advances in multimedia capture, analysis and delivery, combined with the rapid adoption of broadband communication, have resulted in multimedia systems that have advanced traditional forms of education. Research in these areas has achieved impressive results in the last few years and many actual working systems and commercial products are now routinely used by a growing number of people. However, the various web sites and lecture videos produced as part of the e-learning hype generally do not exploit the full potential of multimedia. question of how multimedia can really make learning more exploratory and enjoyable is as yet unanswered, and we are just beginning to understand the real contribution of multimedia to education. In addition, new trends in multimedia technology - such as multimedia on handheld devices or advanced approaches for the automatic analysis of multimodal signals -- offer novel and exciting opportunities for teaching and learning. The growing pervasiveness of multimedia on computing devices also increases the relevance of knowledge about multimedia for computer scientists and software engineers. significance of multimedia for the future of computing, however, is generally not reflected in current curricula. For example, few universities offer dedicated courses, and multimedia is often only taught as part of other courses such as computer vision or machine learning. In addition, multimedia is a very active and rapidly changing field. New and emerging technologies may not only influence how we teach but also have an impact on what we teach. Against this background, we organized the ACM Workshop on Educational Multimedia and Multimedia Education (EMME) 2007. goal of the workshop is to identify current and evolving trends, specify open problems, and discover challenges and prospects for new research in the broad topic of multimedia-based education. By bringing together researchers working on educational multimedia with multimedia educators, we want to establish an open discussion of these issues and create a reference for future research in this area. The call for papers attracted 25 submissions from Asia, the Middle East, Canada, Europe, Australia, and the United States. program committee accepted 14 papers -- 9 full papers for oral presentation and 5 poster presentations -- resulting in an acceptance rate of 36% for oral presentations and 56% overall. submissions truly reflect the diversity of the research currently done in the field. In addition to the presentations on current trends in educational multimedia, we are happy to welcome Susanne Boll, Ramesh Jain, Max Muhlhauser, and Timothy K. Shih, who will discuss teaching multimedia in the workshop's closing panel on The Future of Educational Multimedia.
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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 | 0.002 | 0.003 |
| 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 | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.023 |
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".