Connecting with pre-service teachers’ perspectives on the use of digital technologies and social media to teach socially relevant science
Bibliographic record
Abstract
As an interdisciplinary team of educational researchers we explored pre-service science teachers’ perspectives on using digital technologies and social media to address socially relevant issues in science teaching. The rationale for teaching socially relevant science was embedded in the concept of renaiscience, thus underscoring the need for science to be perceived as a human activity. We drew on generational theory to consider the educational significance of digital technologies and social media. Two different activities were used to elicit the pre-service science teachers’ perspectives. First, we invited them to reflect on a digital animation that we had produced, and they highlighted the advantages of digital animation as a medium to communicate a socially relevant message more appealingly to the Millennial generation. We then engaged these pre-service teachers in a structured concept-mapping activity to consider how digital technologies and social media might be used to address social challenges in South Africa. They drew our attention to the affordances of digital technologies and social media as a means to facilitate critical thinking, cater for diverse learning styles, and make high-quality scientific knowledge more accessible. They highlighted that teaching socially relevant science using digital resources can be cheap, convenient, collaborative, and creative.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".