Mixed Methods Not Mixed Messages: Improving LibGuides with Student Usability Data
Bibliographic record
Abstract
Abstract Objective – This article describes a mixed methods usability study of research guides created using the LibGuides 2.0 platform conducted in 2016 at an urban, public university library. The goal of the study was to translate user design and learning modality preferences into executable design principles, and ultimately to improve the design and usage of LibGuides at the New York City College of Technology Library. Methods – User-centred design demands that stakeholders participate in each stage of an application’s development and that assumptions about user design preferences are validated through testing. Methods used for this usability study include: a task analysis on paper prototypes with a think aloud protocol (TAP), an advanced scribbling technique modeled on the work of Linek and Tochtermann (2015), and semi-structured interviews. The authors introduce specifics of each protocol in addition to data collection and analysis methods. Results – The authors present quantitative and qualitative student feedback on navigation layouts, terminology, and design elements and discuss concrete institutional and technical measures they will take to implement best practices. Additionally, the authors discuss students’ impressions of multimedia, text-based, and interactive instructional content in relation to specific research scenarios defined during the usability test. Conclusion – The authors translate study findings into best practices that can be incorporated into custom user-centric LibGuide templates and assets. The authors also discuss relevant correlations between students’ learning modality preferences and design feedback, and identify several areas that warrant further research. The authors believe this study will spark a larger discussion about relationships between instructional design, learning modalities, and research guide use contexts.
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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 | 0.128 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".