Information Literacy Skills: Promoting University Access and Success in the United Arab Emirates
Bibliographic record
Abstract
The focus of this research is to assess the level of information literacy (IL) skills required for thetransition-to-university experience across the United Arab Emirates (UAE). This research further seeks toshed light on the IL levels of incoming first-year university students and describe their perceptions of theirIL skills. The research population consisted of first-year students from three private universities in the UAE:G1 from Ajman University of Science and Technology (AUST), G2 from Al Ain University of Science andTechnology (AAU), and G3 from Al Hosn University (AHU). The three groups were recruited fromstudents enrolled in first year general education classes. A total of 90 students were asked to take an ILpre-test to assess the level of IL skills they possessed upon entering university. Because the authors arecurrently teaching at AAU, G2 was trained as part of their first-year research skills course at AAU, whilethe other two groups G1 and G3 did not receive IL training. At the end of the semester, the authors usedpost-testing to determine if IL training helped improve IL skills of the trained participants. The post-testwas given to two groups, including G1, which did not receive any training, and G2, the only trained group.Pre-test results identified a gap between the expectations and existing skills vital for secondary anduniversity-level education in all three groups. The post-test evaluation of skills showed statisticallysignificant increases in all IL assessed competencies. The need for customized curriculum to address the ILdeficits revealed by new students is evident.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".