Bibliographic record
Abstract
This study assesses the information literacy (IL) perceptions of instructors at a technical college in the Middle East, the College of the North Atlantic - Qatar. Students at this college are instructed in four areas of study – engineering technology, information technology, business studies and health sciences – which takes place exclusively in English and uses a Canadian curriculum. A web-based survey sent to instructors asked questions in two general areas on their perceptions of student information literacy based on the Society of College, National and University Libraries (SCONUL) definition. Initially, over half of the respondents believed that their students were information literate. However when asked a series of questions about each of the seven IL skills identified by SCONUL, there was a large discrepancy between what skills instructors wished their students achieved, versus what was actually achieved by the end of their programme. Students’ inability to critically evaluate sources of information was seen as the weakest skill by instructors and was considerably lower than the skill level reported by university professors in similar studies. Instructors also conveyed their belief that students lacked strategies when searching for information. When compared to faculty perceptions of students in universities, overall perceptions of IL competency of college students in this study are lower. The study reinforced the need to provide students with tools/strategies to cope with large volumes of information and, when searching, to select appropriate and credible sources of information for both academic and personal uses.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".