MétaCan
Menu
Back to cohort
Record W3198746468 · doi:10.19173/irrodl.v21i3.5387

Distance Learning in Museums: A Review of the Literature

2021· review· en· W3198746468 on OpenAlexvenueno aff
Megan Ennes, Imani Lee

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2021
Typereview
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationInclusion (mineral)Professional developmentBest practiceEducational technologySociologyPedagogyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.145
GPT teacher head0.441
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe International Review of Research in Open and Distributed LearningSame topicMuseums and Cultural HeritageFrench-language works237,207