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Record W3165516041 · doi:10.19173/irrodl.v22i2.5225

Museum-Based Distance Learning Programs: Current Practices and Future Research Opportunities

2021· article· en· W3165516041 on OpenAlexvenueno aff
Megan Ennes

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationTeleconferenceLifelong learningSocial mediaBest practiceCoronavirus disease 2019 (COVID-19)Computer scienceSociologyPedagogyMultimediaWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

Museums play an important role in out-of-school learning. Many museums have begun offering distance learning programs to increase their reach and the accessibility of their collections. These programs serve a wide range of audiences from pre-kindergarten to lifelong learners. This descriptive study examined the current practices in museum-based distance learning programs. Additional data was collected once museums began closing due to COVID-19 and transitioning to distance learning programs. The study found that museums offering programs before COVID-19 predominately offered school-based programs via teleconferencing software. Museums transitioning to distance learning programs following closures due to COVID-19 mainly utilized social media platforms to offer a wide range of programming for the general public. Additional information was gathered regarding how the programs were developed and who facilitated them. Museums are still determining how to respond to COVID-19 closures. This study described the current landscape and potential opportunities for research related to museum-based distance learning programs. These areas for research include establishing best practices, defining high-quality programs, opportunities to engage in instructional design, and professional development for the museum staff facilitating these programs.

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)0.006
metaresearch head score (Gemma)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.450
GPT teacher head0.495
Teacher spread0.045 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations17
Published2021
Admission routes1
Has abstractyes

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