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Record W3168740273 · doi:10.5430/ijhe.v10n6p33

Perceived Life-Readiness from Real-World Curriculum Experiences of Alumni

2021· article· en· W3168740273 on OpenAlexvenueno aff
Caterina Belle Azzarello, Lee Arakawa, daniel houssain edi, Madasyn Sutton, Randy Larkins

Bibliographic record

VenueInternational Journal of Higher Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
FundersUniversity of Northern Colorado
KeywordsPreparednessCurriculumMedical educationPsychologyInclusion (mineral)PedagogyHigher educationSocial psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Students pursue college degrees expecting to learn skills necessary to navigate adult life. While this is the expectation of most students, there is a lack of research examining the perceived effectiveness of real-world applicability in undergraduate degrees.Objective. The purpose of this phenomenological and constructivist study was to explore how college alumni perceive their educational experiences in terms of real-world preparedness.Methods. Eight participants in their mid-twenties (5 females, 3 males) were selected using purposeful sampling. Participants participated in informal, semi-structured, one-on-one Zoom interviews and demographic questionnaire responses.Results. Emerging themes indicated that alumni felt the relationships formed had a greater contribution to their life-readiness compared to their real-world curriculum. Other emerging themes revealed alumni believed they developed valuable skills through hands-on experience and group work. Recommendations were made by alumni for curriculum changes, including smaller class sizes and inclusion of more practical courses.Conclusions. Based on these findings, future research should aim to replicate this study using a broader range of alumni to further investigate this phenomenon, as well as studies that investigate various college types and student experiences.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.424
Teacher spread0.398 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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