Exploring learning opportunities for students in open data portal use across data literacy levels
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
Purpose The purpose of this study is to explore open data portals as data literacy learning environments. The authors examined the obstacles faced and strategies used by university students as non-expert open data portal users with different levels of data literacy, to inform the design of portals intended to scaffold informal and situated learning. Design/methodology/approach The authors conducted an observational user study, in which 14 student participants grouped by self-reported data literacy measures carried out assigned tasks in an open data portal. Data were collected through screen capture, think-aloud protocols and post-session interviews. Findings Participants experienced numerous challenges in finding and using data, with some variation shown between the different literacy groups. The higher data literacy group primarily faced challenges using unfamiliar tools, which may be addressed by improving system usability, while the lower data literacy group struggled due to gaps in basic understanding, which may be addressed by increasing point of need instruction and guidance. Participants used several learning strategies but primarily relied upon trial and error, which was less effective for low data literacy users. Originality/value This study is unique in comparing open data portal use among adult students across data literacy levels through an empirical user study. It contributes methodologically by proposing an instrument for data literacy assessment. It offers a novel perspective on information systems as sites for informal learning and skills development, beyond the immediate goals of system use, and offers concrete suggestions for the future design of open data portals for students and non-expert, citizen users.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.004 | 0.036 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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