Urban Data in the primary classroom: bringing data literacy to the UK curriculum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As data becomes established as part of everyday life, the ability for the average citizen to have some level of data literacy is increasingly important. This paper describes an approach to teaching data skills in schools using real life, complex, urban data sets collected as part of a smart city project. The approach is founded on the premise that young learners have the ability to work with complex data sets if they are supported in the right way and if the tasks are grounded in a real life context. Narrative principles are used to frame the task, to assist interpretation and tell stories from data and to structure queries of datasets. An inquiry-based methodology organises the activities. This paper describes the initial trial in a UK primary school in which twelve students aged 9-10 years learnt about home energy consumption and the generation of solar energy from home solar PV, by interpreting existing visualisations of smart meter data and data obtained from aerial survey. Additional trials are scheduled with older learners which will evaluate learners on more challenging data handling tasks. The trials are informing the development of the Urban Data School, a web-based platform designed to support teaching data skills in schools in order to improve data literacy among school leavers.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.022 | 0.006 |
| 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 it