MétaCan
Menu
Back to cohort
Record W1582179073

Using the Artistic Pedagogical Technology of Photovoice to Promote Interaction in the Online Post-Secondary Classroom: The Students' Perspective.

2012· article· en· W1582179073 on OpenAlexaff
Margaret Edwards, Beth Perry, Katherine J. Janzen, Cynthia Menzies

Bibliographic record

VenueThe Electronic Journal of e-Learning · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPhotovoicePsychologyQualitative propertyPerspective (graphical)CurriculumQualitative researchMathematics educationFocus groupPedagogyData collectionSociologyComputer scienceVisual arts
DOInot available

Abstract

fetched live from OpenAlex

This study explores the effect of the artistic pedagogical technology (APT) called photovoice (PV) on interaction in the online post-secondary classroom. More specifically, this paper focuses on students’ perspectives regarding the effect of PV on student to student and student to instructor interactions in online courses. Artistic pedagogical technologies are teaching strategies based on the arts (Perry & Edwards. 2010). APTs use music, poetry, drama, photography, crafts or other visual media as the basis of teaching activities. Photovoice is the purposeful use of selected visual images and affiliated refection questions as an online teaching strategy. Social Development Theory (Vygotsky, 1978) and Janzen’s Quantum Perspective of Learning (Janzen, Perry & Edwards, 2011) provide the theoretical basis of the study. The convenience sample included 15 graduate students from the Faculty of Health Disciplines at an online university. Participants completed a 4 month master’s course in which PV was used. Data were collected after final course grades were official. Data were gathered using an online questionnaire based on an adaptation (with permission) of Rovai’s (2002) Classroom Cohesion Scale (CSS) and Richardson and Swan’s (2003) Social Presence Scale (SPS). A follow-up focus group with 6 of the original 15 participants was held. Quantitative and qualitative data were collected. This paper focuses on findings from the quantitative data with supportive qualitative comments. Data analysis of the quantitative data takes the form of descriptive statistics. Data analysis of the qualitative data used NVivo software. In sum, the majority of respondents did find that PV had a positive influence on course interactions, but also on their sense of community, comfort in the educational milieu, and on how well they got to know themselves, other learners, and the instructor. Questions for further research are posed.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.046
GPT teacher head0.414
Teacher spread0.368 · 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 designQualitative
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

Citations31
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Electronic Journal of e-LearningSame topicOnline and Blended LearningFrench-language works237,207