The Student’s Voice on Information Literacy Skills: Using the 2017 AASL Standards Framework for Learners
Bibliographic record
Abstract
The 2017 Standards Framework for Learners designed by the American Association of School Librarians offers educators a support guide for creating, implementing and assessing meaningful, structured learning tasks focused on important information literacy skills for students. In this study, we use the Curate element of the AASL Standards Framework for Learners as a lens to analyse students’ voices and experiences while engaged in a Guided Inquiry unit, focusing particularly on their information seeking and use. Findings indicate students have sophisticated understandings of their own information literacy skills, how they engage with information, and the skills needed to be efficient curators of information, but they feel challenged and unconfident about their own skills in completing research tasks. These findings support the role of the school librarian in scaffolding young researchers through this process.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.021 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".