Bibliographic record
Abstract
Distance learning has become an important tool in many fields of education. Museums, like other educational institutions, have been offering distance learning programs to their audiences for more than 30 years. This scoping study examined the published literature related to distance learning programs in museums to inform future research in this field. Searches were conducted in three academic databases in addition to journal hand searches. This resulted in 954 unique citations associated with distance learning in museums. Of these, 17 articles met the criteria for inclusion in the study. Forwards and backwards searches resulted in the addition of two books. A search of the research hosted by the Center for Advancement of Informal Science Education resulted in one additional study for a total of 20 manuscripts. Upon analysis, four major themes were identified. These included benefits and barriers related to distance learning programs in museums, partnerships, and educators’ changing roles as they relate to distance learning programs. Each of these themes is described and areas for future research are identified. Future work should move beyond the predominately evaluative case studies and pursue larger questions about how future research might support museums as they continue to design and implement online programming. This may include exploring best practices in museum-based distance learning and how to develop effective professional development opportunities for the educators engaged in these programs. Such research will enhance museum-based distance learning programs so that they can continue to support global learners.
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.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".