Meaningful Learning at a National Historic Site: How Interpretive Tour Message Content Affects Visitor Learning Transfer
Bibliographic record
Abstract
This study examines meaningful learning transfer at a historic site. Transfer is the ability to apply knowledge to a new situation or setting and can be divided into near and far transfer. Near transfer is characterized by the ability to transfer knowledge to a similar situation, whereas far transfer is the ability to transfer knowledge to a different situation. This between-subject post-test only field experiment investigated the effect of interpretive message design on visitors' ability to transfer leaning from an interpretive audio tour at a heritage site. Interpretive messages were designed to examine the effect of message organizers (i.e. presence or absence of an advance organizer) and message content (i.e. basic, personalized or analogical references) on learning transfer. Visitors to the Winnipeg Exchange District National Historic Site during the 2006 Winnipeg Fringe Theatre Festival were intercepted at the outdoor site and were asked to listen to an interpretive audio tour. After listening to the audio tour participants completed near and far transfer tests. The MANOVA results revealed that no significant differences existed between messages with and without advance organizers with regards to learning transfer. Significant differences were found between personalized messages and basic messages with regards to near and far transfer; furthermore, significant differences existed between analogical reference messages and basic messages with respect to far transfer. These results suggest that near and far transfer are accomplished through different mechanisms and therefore messages need to be carefully designed to accomplish the type of transfer desired. This study provides interpreters with insight into how visitors' meaningful learning can be enhanced at historic sites.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".