Acceptable and Unacceptable Uses of Academic Library Search Data: An Interpretive Description of Undergraduate Student Perspectives
Bibliographic record
Abstract
Objective – This article presents findings about undergraduate student attitudes regarding search data privacy in academic libraries. Although the library literature includes many articles about librarian perceptions on this matter, this paper adds rich, qualitative evidence to the limited research available about student preferences for how libraries should handle information about what they search for, borrow, and download. This paper covers acceptable and unacceptable uses of student search data based on American undergraduate student perspectives. This is an important area of study due to the increasingly data-driven nature of evaluation, accountability, and improvement in higher education, which relies on individual-level student data for learning analytics. These practices are sometimes at odds with libraries’ longstanding commitment to user privacy, which has historically limited the amount of data collected about student use of materials. However, libraries’ use of student search data is increasing. Methods – This qualitative study was approached through interpretive description, a rigorous qualitative framework for answering practical research questions in an applied setting or discipline. I employed the constant comparative method of data collection and analysis to conduct semi-structured interviews with 27 undergraduate students at a large, American, urban public research institution. Interviews included questions as well as vignettes: short scenarios designed to elicit response. Through inductive coding, I organized the data into interpretive themes and subthemes to describe student attitudes. Results – Participants viewed academic library search data as less personally revealing than internet search data. As a result, students were generally comfortable with libraries collecting search data so long as it is used for their benefit. They were comfortable with data being used to improve library collections and services, but were more ambivalent about use of search data for personalized search results and for learning analytics-based assessment. Students had mixed feelings about using search data in investigations related to criminal activity or national security. Most students expressed a desire for de-identification and user control of data. Students who were not comfortable with their search data being collected or used often held their convictions more strongly than those who found the practice acceptable, and their concerns were often related to how data might be used in ways that harm members of vulnerable groups. Conclusion – The results of this study suggested that librarians should further explore student perspectives about search data collection in academic libraries to consider how and if they might adjust their data collection practices to be respectful of student preferences for privacy, while still meeting evaluation and improvement objectives. This study also introduces the qualitative framework of interpretive description to the library and information science literature, promoting use of this applied qualitative approach, which is well-suited to the practical questions often asked in library research studies.
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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 | 0.059 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| 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".