An Evaluation of the Use of the PLUS Model to Develop Pupils' Information Skills in a Secondary School
Bibliographic record
Abstract
Various models of information skills have been developed and applied in schools in North America, Australia, and the United Kingdom in recent years, but there have been few attempts to evaluate the application of the models. This article reports a study of the evaluation of the use of the PLUS model in a secondary school in England. The PLUS model (Herring, 1996; Herring, 1999) categorizes information skills into four interrelated steps: Purpose, Location, Use, and Self-Evaluation. In this study, the PLUS model was used by 112 Year 7 pupils (11-12-year-olds) studying physics. Each pupil completed a questionnaire relating to aspects of information skills and the use of the PLUS model. The views of the school librarian and the physics teacher were gained via semistructured interviews. The main findings of the study were: pupils benefited from using a structured approach to project work; pupils saw the model as a useful tool particularly in helping them to plan, organize, and reflect on their own work; and pupils of this age were able to reflect on both the content and processes of learning.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".