MétaCan
Menu
Back to cohort
Record W2946468090 · doi:10.5220/0007579603130320

Enhancing an Online Digital Storytelling Course for Older Adults through the Implementation of Andragogical Principles

2019· article· en· W2946468090 on OpenAlexaff
Robyn Schell, Diogo da Silva, David Kaufman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFacilitatorDigital storytellingNarrativeContext (archaeology)AndragogyFace (sociological concept)StorytellingPerceptionMultimediaFace-to-facePsychologyComputer sciencePedagogyAdult educationSociologyHistorySocial psychologyArt

Abstract

fetched live from OpenAlex

In earlier research on face-to-face digital storytelling courses for older adults 65 years of age and over, findings showed that this activity provided an opportunity to forge social connections with others through story as well increase the technical proficiency of participants. A digital story is a type of movie that embeds multimedia such as narration, photographs, music and text. In these courses, participants created legacy digital stories that reflected significant events, people and places in their lives. To reach a wider audience, our original face-to-face course was transformed to a fully online course. In this paper, we describe the andragogical approach used for designing this course for older adults and the perceptions of their learning experience within the context of these principles. Our findings show that participants prefer the facilitator take a greater role in discussion forums and providing technical assistance.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.092
GPT teacher head0.436
Teacher spread0.344 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicDigital Storytelling and EducationFrench-language works237,207