Content Matters: How Information Literacy Workshops Tailored for Marginalized Groups Can Impact Student Performance
Bibliographic record
Abstract
Objective – This study sought to understand information literacy instruction tailored for first-year students of color in higher education, and the impact of that instruction on student performance and confidence levels. Methods – The study was conducted at a four-year doctoral-granting higher education institution and was designed as a QUAL+quan convergent mixed-methods study. It utilized critical race theory (CRT) as its theoretical framework, a participatory action research (PAR) approach for its design, and critical pedagogical practices to tailor the instructional content and delivery. The instruction was designed as a multi-session information literacy workshop series delivered outside of the traditional classroom and was comprised of six one-hour sessions: an initial focus group, four information literacy sessions focusing on specific aspects of the research process, and semi-structured interviews. Results – Data collected through discussions, open-ended activities with rubrics, and pre- and post-series surveys were analyzed to determine whether the instructional series impacted student learning outcomes. The results showed the series had a positive impact on student performance and their confidence levels pertaining to understanding and applying information literacy concepts. Conclusion – The study is significant as it is the first to specifically utilize CRT and PAR in a multi-session information literacy workshop series for first-year students of color delivered outside of the traditional classroom setting and can serve as a model for other institutions.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".