Students May Demonstrate Information Literacy Skills Following Library Instruction
Bibliographic record
Abstract
A Review of: Luetkenhaus, H., Hvizdak, E., Johnson, C., & Schiller, N. (2017). Measuring library impacts through first year course assessment. Communications in Information Literacy, 11(2), 339-353. http://comminfolit.org/index.php Abstract Objective – To determine whether there is a correlation between information literacy skill development and participation in one or more library instruction sessions. Design – Learning outcomes assessment. Setting – A public research institution with multiple campuses. Subjects – 244 first-year undergraduates enrolled in a compulsory general education course during the 2014-2015 academic year. All subjects completed a series of library research assignments, followed by a final research paper. 65% of subjects participated in at least one library instruction session as part of the course, and 35% did not. Methods – The researchers convened six librarians and six instructors/faculty to score 244 research papers using a rubric designed to measure six possible information literacy learning outcomes. Evaluators established inter-rater reliability through a norming session, and each artifact was scored twice. The authors analyzed rubric scores using Ordinary Least Squares regression modeling. Main Results – Participation in a library instruction session correlated with higher rubric scores in three information literacy learning outcomes: argument building; source type integration; and ethical source citation. Conclusion – Students may achieve greater information literacy learning outcomes when they participate in course-integrated library instruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".