Information Literacy Instruction Assessment and Improvement through Evidence Based Practice: A Mixed Method Study
Bibliographic record
Abstract
Objective— This study explored first-year students’ learning and satisfaction in a required information literacy course. The study asked how students understand connections between themselves and information literacy in terms of power, society, and personal relevance to assess if students’ understanding of information literacy increased after taking the course. Student satisfaction with the course also was measured. Methods—The study used pre- and post tests and focus group session transcripts which were coded and analyzed to determine student learning and satisfaction during the regular 2008-2009 academic year at California State University, East Bay. Results— Many students entered the course without any concept of information literacy; however, after taking the course they found information literacy to be personally relevant and were able to articulate connections among information, power, and society. The majority of students were satisfied with the course. The results from analyzing the pre- and post-tests were supported by the findings from the focus group sessions. Conclusion— The results of this study are supported by other studies that show the importance of personal relevancy to student learning. In order to fully assess information literacy instruction and student learning, librarians should consider incorporating ways of assessing student learning beyond testing content knowledge and levels of competency.
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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 | 0.052 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".