Enhancing Engineering Outreach With Interactive Game Assessment
Bibliographic record
Abstract
The need to educate general public about technology grows with broadening gap between technology use and its understanding in a consumer society.One of the effective venues for such education is that of hands-on museums, which engage a wide spectrum of visitors.This paper reports on the use of a data collection mechanism embedded in an interactive museum exhibit that highlights principles of modern consumer product engineering.The exhibit is comprised of a set of computer games and complementary physical displays.The games have a built-in data collection system tracking users' actions while playing the game.Collected data allows for demographic analysis of visitor population, user performance assessment, and provides game-play perspective useful for effective game design.Presented results are based on a year-long study involving about 17,000 museum visitors. Outreach in the Form of a Museum Exhibit: Overview of the ProjectGovernment funding supports research work on the cutting edge of manufacturing technologies, but the general population's understanding of manufacturing processes, equipment, and careers lags far behind that edge.To bridge the gap, the NSF Engineering Research Center for reconfigurable Manufacturing Systems (ERC/RMS) at the University of Michigan invested in the creation of a museum exhibit to be installed at the Ann Arbor Hands-On Museum, a children's science center.Informal learning environments, like museums, align well with outreach efforts because they share many goals: to intrigue, educate, and inspire visitors.Science museums in particular have become more conscious of their role as an auxiliary to the education that occurs in traditional classrooms, striving to encourage interest in science 10 and to present science policy issues 9 that might not get addressed in the classroom.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".