Designing an adaptive multimedia interactive to support shared learning experiences
Bibliographic record
Abstract
With the aid of new technologies, integrated design approaches are becoming increasingly incorporated into exhibit design in museums, aquaria and science centres. These settings share many similar design constraints that need to be addressed when designing multimedia interactives as exhibits. The use of adaptive systems and techniques can overcome many of the constraints inherent in these environments as well as enhance the educational content they incorporate. Our main design goal was to facilitate a process to create user centric, collaborative and reflective learning spaces around the smart multimedia interactives. We were interested in encouraging deeper exploration of the content than what is typically possible through wall signage, video display or a supplemental web page. We discuss techniques to bring adaptive systems into public informal learning settings, and validate these techniques in a major aquarium with a beluga simulation interactive. The virtual belugas, in a natural pod context, learn and alter their behavior based on contextual visitor interaction. Data from researchers, aquarium staff and visitors was incorporated into the evolving interactive, which uses physically based systems for natural whale locomotion and water, artificial intelligence systems to simulation natural behavior, all of which respond to user input. The interactive allows visitors to engage in educational "what-if" scenarios of wild beluga emergent behavior using a shared tangible interface controlling a large screen display.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".