Using the Artistic Pedagogical Technology of Photovoice to Promote Interaction in the Online Post-Secondary Classroom: The Students' Perspective.
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".