Information Literacy and Retention: A Case Study of the Value of the Library
Bibliographic record
Abstract
Objective - The authors investigated the impact of library instruction on information literacy (IL) skills as part of ACRL’s AiA initiative. Additionally, the researchers sought to determine whether there was a relationship between IL tests scores and research experiences with student success outcomes such as retention. Methods - The researchers administered a standardized IL test to 455 graduate and undergraduate students in multiple disciplines. They then collected outcome data on GPA, retention, and graduation three years later. Results - While there were no significant differences between those students who had instruction and those who did not on the IL test, a regression analysis revealed that experience writing research papers that required library resources and an individual’s use of library books throughout their academic career demonstrated significant, positive relationships with whether a student passed the information literacy test. Additionally, using the longitudinal data on GPA, retention, graduation, and employment, the researchers found that students’ IL scores were significantly correlated with their GPAs, and that students who passed the IL test were more likely to be retained or graduate within six years. Conclusion - The ability to demonstrate IL skills appears to contribute to retention and graduation and, therefore, may be an integral part of one’s academic success. Further, experience writing research papers and other meaningful assignments contributes to student success.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".