Lessons Learned: A Reflection of Five Years of Engaging Educators with Digital Museum Resources
Bibliographic record
Abstract
This article reports lessons learned from educator need-centered professional development offerings (PD) on accessing and using digital museum resources through the Smithsonian Learning Lab (SLL), a free, interactive platform for discovering digital resources, creating content with online tools, and sharing with communities of learners. Since the platform launched in late 2015, the Smithsonian Office of Education Technology has engaged more than 20,000 educators on the use of the SLL through PD that was offered both in-person and digitally, synchronously and asynchronously, frequently through partners within the network of Smithsonian Affiliate museums. Results from more than 1,100 aggregated surveys, 50 in-depth interviews, and five focus groups were triangulated and demonstrated that PD was associated with increased participants’ awareness of, skills in, and frequency in using the SLL, creating, and sharing content, and overall satisfaction with the platform. Educators, especially those who participated in PD, agreed with statements about successfully achieving student learning outcomes when using SLL in classrooms. Findings highlighted the importance of cultivating long-term, supportive relationships with PD participants and partners and offering consistently available support with museum staff well beyond the workshops. They also pointed to the value of user-centered marketing research and strategies to broaden the reach of digital learning resources. As museums and cultural organizations work to meet the increasing demand to engage educators digitally, these concrete lessons can be adapted and applied to institutions that provide access to digital resources to educators.
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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 | 0.025 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".