Teaching Literacy: Methods for Studying and Improving Library Instruction
Bibliographic record
Abstract
Objective – The aim of this paper is to evaluate teaching effectiveness in one-shot information literacy (IL) instruction sessions. The authors used multiple methods, including plus/delta forms, peer evaluations, and instructor feedback surveys, in an effort to improve student learning, individual teaching skill, and the overall IL program at the American University in Cairo.
 
 Methods – Researchers implemented three main evaluation tools to gather data in this study. Librarians collected both quantitative and qualitative data using student plus/delta surveys, peer evaluation, and faculty feedback in order to draw overall conclusions about the effectiveness of one-shot IL sessions. By designing a multi-method study, and gathering information from students, faculty, and instruction librarians, results represented the perspectives of multiple stakeholders.
 Results – The data collected using the three evaluation tools provided insight into the needs and perspectives of three stakeholder groups. Individual instructors benefit from the opportunity to improve teaching through informed reflection, and are eager for feedback. Faculty members want their students to have more hands-on experience, but are pleased overall with instruction. Students need less lecturing and more authentic learning opportunities to engage with new knowledge.
 
 Conclusion – Including evaluation techniques in overall information literacy assessment plans is valuable, as instruction librarians gain opportunities for self-reflection and improvement, and administrators gather information about teaching skill levels. The authors gathered useful data that informed administrative decision making related to the IL program at the American University in Cairo. The findings discussed in this paper, both practical and theoretical, can help other college and university librarians think critically about their own IL programs, and influence how library instruction sessions might be evaluated and improved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.881 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".