A Critical Evaluation of Year 7 Students’ Reflections on the Use of Information Skills when Completing a Curriculum Related Project
Bibliographic record
Abstract
While there have been a large number of books, articles and reports on information literacy in schools, there is a lack of empirical evidence in relation to the use of information literacy models in schools, Wolf (2003) and Herring, Tarter and Naylor (2000 and 2002) being exceptions. The purpose of this pilot study was to examine students’ reflections on the information skills process following the use of an information skills scaffold (the PLUS model) during the completion of an assignment in a secondary school in the UK. The study takes a constructivist approach and data was gathered via a post-assignment questionnaire given to students. Results show that students’ confidence was variable at the start of the assignment; that students found the completion of a concept map to be generally helpful; that almost 50% of students found that completing a concept map made them more confident; that students used the concept map when searching for information; that students used a range of print and electronic sources of information; that students’ preferences for using particular types of resources included both print and electronic sources; that students found the PLUS model to be helpful with their project; and that most students were prepared to consider using the model when completing future assignments.
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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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".