Assessing the Impact of an Information Literacy Course on Students' Academic Achievement: A Mixed-Methods Study
Bibliographic record
Abstract
Objective – The aim of this study is to demonstrate the impact of a stand-alone, credit-bearing information literacy course on retention and GPA for students at an open access urban college. Methods – Researchers conducted a mixed-methods study with a two-part focus. The first examined the impact of a credit-bearing course using propensity score matching (PSM) techniques to compare academic outcomes for students who participated in the course versus outcomes for similar students who did not enroll in the course. Multiple regression analyses were conducted to measure impact on GPA and performance in 100-level introductory English general education courses. Logistic regression analysis was used to determine persistence one year after enrolling in the course. The second part utilized a questionnaire to survey students of this targeted group to determine impact of the course on their information-seeking behaviour in subsequent academic courses and for non-academic purposes. Results – The quantitative analyses showed: (a) a higher GPA, though slight, for students who have taken the course over the matched comparison group; (b) an increase in persistence for students who have taken the course over the matched comparison group after one year of taking the course; but (c) lower performance in 100-level introductory English courses by students who have taken the course in contrast to the matched comparison group. Qualitative data provided through the questionnaire revealed positive and substantive reflective statements that support learning outcomes of the course. Conclusion – The findings in this study underscored the importance of a stand-alone, credit-bearing information literacy course for undergraduate students, particularly for first-generation students attending an open access urban institution. The findings also demonstrate the academic library’s contribution to institutional retention efforts in support of students’ academic 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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".