MétaCan
Menu
Back to cohort
Record W4328100417 · doi:10.18438/eblip30138

Data Literacy in the Social Sciences: Findings from a Local Study on Teaching with Quantitative Data in Undergraduate Courses

2023· article· en· W4328100417 on OpenAlex
Patricia Condon, Eleta Exline, Louise Buckley

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEvidence Based Library and Information Practice · 2023
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsnot available
Fundersnot available
KeywordsInformation literacySet (abstract data type)Sample (material)LiteracyMathematics educationExploratory researchScientific literacyPsychologyMedical educationComputer scienceSociologyPedagogyScience educationSocial scienceChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.078
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.390
GPT teacher head0.518
Teacher spread0.128 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it