Undergraduate Students Still Experience Difficulty Interpreting Library of Congress Call Numbers
Bibliographic record
Abstract
Objective – To explore how undergraduate students interpret Library of Congress call numbers when trying to locate books. Design – Multiple case study. Setting – A public, residential university in Illinois, United States of America. Subjects – 11 undergraduate students (10 upper division, 1 freshman; no transfer students included). Methods – A qualitative approach was adopted, with a multiple case study design used to facilitate the collection of data from several sources. Students were recruited for the study via convenience and snowball sampling. Participants who volunteered were interviewed and requested to complete a task that required them to organize eight call numbers written on index cards in the correct order. Interviewees were also asked about any instruction they had received on interpreting call numbers, and their experiences locating materials in other libraries and bookstores. Responses were then coded using colours to identify common themes. Main Results – The study reported that there was little correlation between the students’ own estimation of their ability to locate materials and their actual performance in the index card test. Five students who reported that they could find materials 75-100% of the time performed poorly in the test. Of the 11 participants, only 4 ordered the cards correctly, and in 1 such case this was by fortune rather than correct reasoning. Of these, three self-reported a high level of confidence in their ability to locate material, whilst one reported that he could only find the material he was looking for approximately half of the time. Of the seven students who incorrectly ordered the cards, no two students placed their cards in the same order, indicative that there is no clear pattern in how students misinterpret the numbers. During the interview process, five students stated that they experienced more difficulty locating books in bookstores compared with the library. Conclusion – Based on the findings of the study, the authors recommend several interventions which could help students to locate material within the library, namely through improved signage in shelving areas including the listing of subjects and colour-coding, as well as integrating training on understanding call numbers into subject-based instruction. The possibility of using online directional aids such as QR codes and electronic floor maps is also suggested as a strategy to help orient students.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".