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Record W2935731493

Tuning into General Education: Exploring Student Experience in Undergraduate Education

2018· article· en· W2935731493 on OpenAlexaffabout
Sam Ulmer-Krol

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNumeracyHigher educationFocus groupPedagogyPsychologyMedical educationMathematics educationSociologyPublic relationsPolitical scienceMedicine
DOInot available

Abstract

fetched live from OpenAlex

Existing in a growing number of Canadian institutions, general education (GE) involves mandatory course requirements that fall outside of a student’s discipline concentration and emphasizes skills in numeracy, critical thinking, and communications.  Previous studies suggest that students have been notably absent from most efforts to revise and assess GE programming. Key researchers further affirm the need to investigate student experiences within their respective universities to facilitate meaningful change in GE assessment and better prepare students to meet the challenges of the 21st century. Funded by the Social Science and Humanities Research Council of Canada (SSHRC), this paper will report on findings from a case study investigating students’ experiences learning within a university committed to GE through two primary phases. The first stage made use of document analysis and interviews with administrators and faculty to understand how the university envisions GE, and the second stage engaged students through surveys and focus groups to investigate how they experience and make sense of GE. Though data collection is ongoing, this study will inform and allow for greater and more impactful assessment and change to reconcile institutional GE mandates with how they are experienced.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.120
GPT teacher head0.403
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2018
Admission routes2
Has abstractyes

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