Measuring the Value of Library Resources and Student Academic Performance through Relational Datasets
Bibliographic record
Abstract
Objective – This article describes a project undertaken by the University of Wollongong Library (UWL) to identify whether a correlation exists between usage of library resources and academic performance. Methods – A multidimensional approach to systems design was implemented, requiring collaboration between among the library, university administration, Performance Indicator Project team (PIP), and information technology services. The project centers on the integration and interrogation of a series of discrete datasets containing student performance, attrition, demographic, borrowing, and electronic resources usage data. PIP built a cube for the library that links usage of library resources to student demographic data and academic performance (the “Library Cube”). Other cubes will be linked later. Results – While initial reports are rudimentary and do not yet incorporate data on e-resource usage, results are favourable in demonstrating the value of using the library information resources in coursework. Based on the data generated to date, students who borrow library resources do outperform students who do not. Early trend data shows up to a 12-point difference in grades. Conclusion – The Library Cube signals a new milestone in the UWL’s quality assessment journey. Well-established measures of effectiveness and efficiency will be further complemented by measures of impact and value, allowing the library to step even closer to the goal of having effective and valued partnerships with the university community to realize teaching, learning, research, and internalization goals.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".