Information Literacy Skills Are Positively Correlated with Writing Grade and Overall Course Performance
Bibliographic record
Abstract
A Review of: Shao, X., & Purpur, G. (2016). Effects of information literacy skills on student writing and course performance. The Journal of Academic Librarianship, 42(6), 670-678. http://dx.doi.org/10.1016/j.acalib.2016.08.006 Abstract Objective – To measure the correlation of tested information literacy skills with individual writing scores and overall course grade. Design – Online, multiple-choice survey. Setting – Public research university in North Carolina, United States of America. Subjects – Freshmen students enrolled in either first-year seminar (UCO1200) or basic English writing course (ENG1000). Methods – A 25-question, forced-choice test was piloted with 30 students and measured for internal consistency using Cronbach’s Alphas. The survey instrument was slightly revised before being administered online via SelectSurvey, to 398 students in 19 different sections of either UCO1200 or ENG1000, during class sessions. The test measured students’ information literacy skills in four areas: research strategies, resource types, scholarly vs. popular, and evaluating websites. The preliminary questions asked for each student’s name, major (by category), number of library instruction sessions attended, and the names of library services utilized. The students’ information literacy scores were compared to their writing scores and overall course grades, both of which were obtained from course instructors. The information literacy scores were also analyzed for correlation to the number of library instruction sessions attended or the types of library services utilized. Main Results – Information literacy skills positively correlated with writing scores (n=344, r=-.153, p=0.004) and final course grades (n=345, r=0.112, p=0.037). Pearson’s Correlation Coefficients results demonstrated relationships between writing scores and the information literacy test section “Scholarly versus Popular Sources” (n=344, r=0.145, p=0.007), and final grade and information literacy test sections “Types of Sources” (n=345, r=0.124, p=0.021) and “Website Evaluation” (n=345, r=0.117, p=0.029). The impact of using other library services or of attending multiple information literacy sessions was not statistically significant. Conclusion – Students’ mastery of tested information literacy skills directly correlates to their writing and final course grades. The study confirms the need for faculty and library collaboration to create well-integrated library instruction and services, and advocates for librarians to become integral to campus initiatives for student learning and 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".