Charting Success: Using Practical Measures to Assess Information Literacy Skills in the First-Year Writing Course
Bibliographic record
Abstract
Abstract Objective – The aim was to measure the impact of a peer-to-peer model on information literacy skill-building among first-year students at a small commuter college in the United States. The University of New Hampshire (UNH) is the state’s flagship public university and UNH Manchester is one of its seven colleges. This study contributed to a program evaluation of the Research Mentor Program at UNH Manchester whereby peer writing tutors are trained in basic library research skills to support first-year students throughout the research and writing process. Methods – The methodology employed a locally developed pre-test/post-test instrument with fixed-choice and open-ended questions to measure students’ knowledge of the library research process. Anonymized data was collected using an online survey with SurveyMonkey™ software. A rubric was developed to score the responses to open-ended questions. Results – The study indicated a positive progression toward increased learning for the three information literacy skills targeted: 1) using library resources correctly, 2) building effective search strategies, and 3) evaluating sources appropriately. Students scored higher in the fixed-choice questions than the open-ended ones, demonstrating their ability to more effectively identify the applicable information literacy skill than use the language of information literacy to describe their own research behavior. Conclusions – The assessment methodology used was an assortment of low-key, locally-developed instruments that provided timely data to measure students understanding of concepts taught and to apply those concepts correctly. Although the conclusions are not generalizable to other institutions, the findings were a valuable component of an ongoing program evaluation. Further assessment measuring student performance would strengthen the conclusions attained in this study.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".