Auditing information literacy skills of secondary school students in Singapore
Bibliographic record
Abstract
The aim of this study was to assess the information literacy (IL) and cyber-wellness skills of secondary 3 (grade 9) students, who are aged 14-15, in Singapore. The Ministry of Education in Singapore has introduced aspects of IL in schools through incorporating components into the syllabi of various subjects. A pilot-tested online survey, validated by IL experts from Canada, Hong Kong, Kuwait and Thailand, was used for data collection. The survey was taken by 2,458 students from 11 secondary schools in different geographical zones of Singapore. It was found that the use of school libraries and their resources was at a very low level. The majority of the students approached classmates and friends for help in solving their information-related problems. Only a small fraction consulted their school librarian. The overall IL assessment score showed that the students possessed a ‘middle’ level of IL skills which is better than previous (pre-curriculum integration) IL assessment studies in Singapore. As curriculum-embedded IL skills are taught by subject teachers, their level of preparedness could be a matter of concern. Similarly, fragmentation of IL concepts in different subject textbooks may cause co-ordination problems among teachers. This paper highlights the need for developing a roadmap for providing IL skills at different grade levels and in different subject areas. It is expected that the findings of this study will be useful to curriculum planners, teachers, schools librarians and others involved in IL education.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".