Information Literacy Skills of First-Year Library and Information Science Graduate Students: An Exploratory Study
Bibliographic record
Abstract
Abstract Objective – This cross-sectional, descriptive study seeks to address a gap in knowledge of both information literacy (IL) self-efficacy and IL skills of students entering Louisiana State University’s Master of Library and Information Science (MLIS) program. Methods – An online survey testing both IL self-efficacy and skills was administered through Qualtrics. The online survey instrument used items from existing instruments (Beile, 2007; Michalak & Rysavy, 2016) and was distributed to two cohorts of incoming students; the first cohort entered the MLIS program in fall 2017, and the second entered in spring 2018. Results – Data varied between cohorts and between survey instruments for both IL self-efficacy and skills; however, bivariate analysis of data indicated a moderate positive correlation between overall IL self-efficacy and demonstrated IL skill scores in both fall 2017 and spring 2018 cohorts. Conclusion – The study indicates a need for a larger, multi-institutional study using a rigorously validated instrument to gather data and make generalizable inferences about the IL self-efficacy and skills of incoming LIS graduate students.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".