How high-school students find and evaluate scientific information: A basis for information literacy skills development
Bibliographic record
Abstract
This study examined the relationship between curricula in secondary-level science classrooms, which support development of information literacy skills, and actual student skills. A vast body of research reflects deep concern with the level of information literacy skill development among secondary and post-secondary students. But even when educational curricula mandate skill development, many students are unable to demonstrate sophisticated information searching and critical evaluation skills. The findings of this study, which we based on analyzing information seeking tasks and conducting interviews with students in three biology classes in a large urban high school, demonstrated a similar lack of skills. Pressure on teachers to “teach to examinations”—that is, to focus on substantive content rather than on information literacy skills and information literacy skills deficits among teachers themselves—is a possible explanation for these results. The study is of particular interest to teachers of the curriculum applicable in the study context, but the broader implications of repeated indications of gaps in students' information literacy skills are a significant indicator that schools must assume a larger responsibility for information literacy instruction . Leaving skill development to the post-secondary environment will not ensure that citizens are sufficiently skilled to participate fully in 21st century life, in workplaces or in their personal life contexts.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".