Social Media and Critical Digital Museum Pedagogy: A Multicase Study of Institutions in Alberta
Bibliographic record
Abstract
The pedagogical dimensions of social media have been a topic of discussion in the museum sector for nearly 4 decades (Crow & Din, 2010). Recently, however, widespread institutional closures prompted by the onset of the COVID-19 pandemic have resulted in a rapid transformation in how and why museums engage with social media. A particularly notable development has been a surge in social media adoption, reported by over 50% of institutions (International Council of Museums, 2020). Despite its increasing popularity amongst museums, uses of social media are still being shaped by continuously evolving digital platforms, neoliberal institutional imperatives, and diverse organizational priorities—factors that complicate effective and ethical social media use (Kidd, 2011). At the same time, the educational dimensions of museum social media practices remain largely underexamined and a more nuanced understanding of the informal and nonformal learning facilitated by museums’ social media engagement is critically needed. This study was driven by the following research question: How do museums and museum educators understand and engage with critical social media pedagogy? Further sub-questions included: How do museums engage in nonformal pedagogical activities on social media? What values and priorities drive these approaches? What assumptions or intentional choices about their audiences do museums make when developing content to engage with learners? Do competing priorities or interests influence museums’ approaches to social media pedagogy? How does social media facilitate informal learning about museums and their role in the community? The study was grounded in the field of Adult Education, focusing on public pedagogy and digital education. I adopted a multicase study methodology to provide in-depth insights into the phenomenon at the centre of this research by examining three large museums in Alberta, Canada. Data was gathered from three sources: online museum publications, social media content created by museums, and practitioner perspectives through semi-structured interviews and focus groups. I examined the data within a critical theoretical framework to apply a socially oriented, critical lens to practitioner and institutional perspectives surrounding this phenomenon. The findings are presented according to five tensions that emerged from the data as findings: conflicts between marketing and pedagogical objectives; balancing nonformal self-directed learning and collective learning; community-centred and authoritative pedagogical approaches; pleasing learners and critical pedagogical objectives; and quantitative or qualitative measures of success.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".