Mobile interpretive apps as educational mediating tools in science education: participant-based digital design in natural history and science museums
Bibliographic record
Abstract
The use of mobile and social learning media for K-12 students continues to rapidly increase in both formal and informal learning environments. While many educational apps have been developed for adult visitors to museums and science and technology centres (STCs), very few programs exist that are specifically designed to meet the unique learning and interpretive needs of elementary students in these learning environments. This dissertation explores the inclusion and development of children’s ideas and digitally mediated interpretive activities for peers within the exhibits of the natural history gallery at the Royal British Columbia Museum (RBCM) in Victoria, British Columbia. In this triangulated case study, thirteen Grade 4 and 5 students, five museum interpreters, and six elementary teachers worked in teams to design educational apps for their peers using experimental software specifically designed for this project. Five design teams composed of 2-3 students, one teacher, and a museum educator designed a wide variety of science activities for the natural history gallery at the RBCM. The results of analytic triangulation indicate that mobile interpretive apps acted as imperfect but important educational mediating tools for the participants in this study. The analysis revealed that, despite initial preconceptions and frustrations students and educators had about mobile design and technologies, Grade 4 and 5 elementary students were capable and highly interested creating mobile science apps for the natural history galleries at RBCM. Students and educators designed content and activities that extended participant-based learning opportunities beyond the existing science programs and curriculum currently available at the RBCM. The dissertation concludes with an examination of how informal science institutions can move beyond educational interactivity to more participatory frameworks that include the ideas and voices of young people within mobile learning and educational app development at natural history museums and STCs in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".