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Record W3091921298 · doi:10.20343/teachlearninqu.8.2.7

Between Culture and Curricula: Exploring Student and Faculty Experiences of Undergraduate Research and Inquiry

2020· article· en· W3091921298 on OpenAlexaffabout
Andrew Perrella, Huyen Dam, Lynn Martin, John C. Maclachlan, Nancy Fenton

Bibliographic record

VenueTeaching & Learning Inquiry The ISSOTL Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsUndergraduate researchNexus (standard)CurriculumDiversity (politics)PedagogyHigher educationFaculty developmentMedical educationPerceptionProfessional developmentSociologyPsychologyPolitical scienceMedicineEngineering

Abstract

fetched live from OpenAlex

Undergraduate research and inquiry is a growing movement within the teaching and learning nexus, with many institutions developing their practices within this culture of education. This study aimed to identify the perceptions and experiences surrounding undergraduate research and inquiry among students and faculty at McMaster University in Ontario, Canada; assess the extent of research and inquiry in the design of undergraduate courses; and explore the facilitators and obstacles educators encounter when attempting to implement this educational approach. Although differences exist in student and faculty definitions of undergraduate research and inquiry, we found two principal models that characterize its structure and delivery—the scaffold model and bookend model. A third, the abstract model, does not employ the practice of inquiry. Despite numerous benefits unique to undergraduate research and inquiry education identified by stakeholders, notable barriers (such as funding, faculty buy-in, limited student experience, and inherent competition) hinder its progress. Overall, we found a diversity of undergraduate research and inquiry practices across the university, operating within varying cultures and comfort levels, which suggests unequal access for student learners.

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.

How this classification was reachedexpand

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)0.014
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.527
GPT teacher head0.539
Teacher spread0.011 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2020
Admission routes2
Has abstractyes

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