An Analysis of Museums Using the Contextual Model of Learning to Aid in the Development of STEAMtank at ASU
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
abstract: Researchers John H. Falk and Lynn D. Dierking developed what they call the Contextual Model of Learning in their 2012 publication, The Museum Experience Revisited. This model emphasizes the significance of the visitor experience in the museum industry and is defined as three interconnected contexts that constitute a museum visitor’s experience. These contexts are the personal context, the sociocultural context, and the physical context. Falk and Dierking argue that all three contexts must be properly acknowledged by the museum for a positive visitor experience. They also provide readers with several recommendations on effective design strategies that fit within the principles of the Contextual Model of Learning. In this analysis, these principles are related directly to museums today. The Field Museum in Chicago and The Children’s Museum of Phoenix are noted for having exceptional websites. The Royal Ontario Museum and the Asian Art Museum are mentioned for having engaging marketing strategies. The Black Country Living Museum in the United Kingdom and the Museum of Modern Art in New York are recognized for innovative social media use. The USS Midway Museum in San Diego and the Musical Instrument Museum in Phoenix are acknowledged for their excellent designs, media usage in exhibits, and accessibility options. The British Museum in London is mentioned for its virtual experiences and gift shop. The Metropolitan Museum of Art is also mentioned for its gift shop. The Arizona Science Center and the Children’s Museum of Indianapolis are commended for their programs. Finally, a brief discussion is done on STEAMtank, a museum experience in development at Arizona State University, and how the principles within the Contextual Model of Learning are being integrated in similar fashion to the other museums discussed. (abstract)
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it