Data Literacy in the Social Sciences: Findings from a Local Study on Teaching with Quantitative Data in Undergraduate Courses
Bibliographic record
Abstract
Objective – The University of New Hampshire (UNH) Library conducted an exploratory study of the pedagogical practices of social science instructors at UNH who teach using quantitative data in undergraduate courses. This study is connected to a suite of parallel studies at other higher education institutions that was designed and coordinated by Ithaka S+R. The four aims of this study were to explore the ways in which instructors teach and engage undergraduates in the social sciences using quantitative data; understand the support needs of these instructors; develop actionable recommendations for campus stakeholders; and identify opportunities for the development of resources, services, or activities in the library to support the use of quantitative data in the classroom. Methods – For the UNH study, the research team recruited eleven participants through convenience sampling for one-on-one, semi-structured interviews. The study sample included lecturers, assistant professors, associate professors, and full professors across seven social science disciplines from the Durham and Manchester campuses. Results – Courses using data provide a unique opportunity for students to gain experience by working with hands-on examples. The two overarching themes identified speak to both the motivations of instructors who teach with data and the challenges and opportunities they face: teaching with data for data literacy and scientific literacy and teaching with data for statistical, data, and tools skill building. Conclusion – Data literacy is an important set of competencies in part because of the quality and quantity of data students encounter; they need to have the ability to critically evaluate data, methods, and claims. This study directed attention to an area that had not previously been examined at UNH and is an important first step toward creating greater awareness and community of practice for social science instructors teaching with data. The UNH Library offers research data services and is exploring new ways of supporting data literacy. UNH has opportunities to create additional supports for instructors and students that could improve student learning outcomes. Such efforts may require cross-college or cross-department coordination as well as administrative support.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".