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Record W2751241875 · doi:10.47678/cjhe.v47i2.186456

Experiences of Students Enrolled in Integrated Collaborative College/University Programs

2017· article· en· W2751241875 on OpenAlexafffundvenue
Janet Landeen, Nancy Matthew‐Maich, Leslie Marshall, Lisa-Anne Hagerman, Lindsay Carrocci Bolan, Maurine Parzen, Maria Pavkovic, Christine Riehl, Joshua Lucas de Carvalho, Natasha Bilau, Zetian Zhang, Sheri Oliver, Jacob Cottreau, Bhavin Shukla

Bibliographic record

VenueCanadian Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsConestoga CollegeMohawk CollegeMcMaster University
FundersMcMaster University
KeywordsFocus groupCurriculumMedical educationPerceptionPsychologyHigher educationCollaborative learningQualitative researchPedagogyMathematics educationSociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Little is known about the student experience in collaborative college/university programs, where students are enrolled in two institutions simultaneously in integrated curriculum designs. This interpretive, descriptive, qualitative study explored these students’ perspectives. Sixty-eight participants enrolled in one of four collaborative programs from three different faculties engaged in student researcher-led focus groups. Results revealed that while all participants valued their respective academic programs, their day-to-day life experiences presented a different story. Some students had perceptions of belonging and thrived in a dual world. Others had perceptions of ambiguous belonging, which contributed to them perceiving themselves through a perpetual lens of being less than university-only students. Issues of how students are invited to engage in the university and college cultures, perceptions of power and control, and daily reminders of being different all contributed to positive or ambiguous student identities. The results raise preliminary questions for universities and colleges regarding how to enhance the student experience in these collaborative programs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.992

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 studies0.0010.001
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 head0.026
GPT teacher head0.390
Teacher spread0.364 · 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 designObservational
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

Citations3
Published2017
Admission routes3
Has abstractyes

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