Investigating the efficacy of embedment: experiments in information literacy integration
Bibliographic record
Abstract
Purpose This paper seeks to present the findings of a series of experiments in embedding a librarian at a variety of levels into the undergraduate classroom. This study aimed to determine whether different levels of librarian embedment correlated with improvement in undergraduate students' information literacy (IL) skills. Design/methodology/approach Students from five undergraduate History courses and two undergraduate Women's Studies courses participated in the study. A librarian was embedded in each of the courses, at a variety of levels across courses. All student assignments were graded using a standardized rubric. Students' IL skills were assessed using the research component of the rubric, which measured their ability to locate, retrieve, evaluate, and incorporate sources into their assignments. Students' research and overall scores on their initial problem‐based learning (PBL) assignments and written assignments were compared to their final assignment scores in order to assess improvement over the course of the term. Findings There was significant improvement in students' scores when a librarian was conspicuously and obviously embedded in the academic classroom. Students' scores showed little improvement when the librarian was embedded but not explicitly identified as a specialist in information literacy, and when the researchers attempted to embed information literacy seamlessly in the classroom. Research limitations/implications The research was conducted only in courses in the Humanities. Practical implications This study suggests that students' IL skills improve most when IL is identified in the classroom as a specialized subject taught by a highly trained specialist. The methodology used may be useful for others studying the impact of IL instruction. Originality/value Although embedding has been studied and reported on in the literature in a variety of contexts, the study of different levels of embedding, quantified using the same rubric is unique.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".