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Record W4412703122 · doi:10.33009/fsop_jpss137478

The Role of Student Involvement and Engagement in Rural and Urban High School Environments on Postsecondary Transition and Academic Success

2025· article· en· W4412703122 on OpenAlexaffabout
Victoria Parohl, Michelle Prytula

Bibliographic record

VenueJournal of Postsecondary Student Success · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTransition (genetics)Student engagementPostsecondary educationPedagogyMathematics educationPolitical scienceHigher educationPsychologyChemistry

Abstract

fetched live from OpenAlex

In Canada’s diverse educational landscape, students transition from various high school environments is influenced by socioeconomic, demographic, and community-specific factors. This diversity affects their preparedness for higher education and presents both challenges and opportunities for higher education institutions to ensure rich and successful student experiences by offering effective transition support. While many studies explore the connection between secondary or postsecondary involvement and success, there is a gap in research comparing how high school involvement in rural versus urban settings has an impact on postsecondary success. This study examined the impact of high school involvement—measured by hours spent on homework, classroom participation, and extracurricular activities—on students’ integration and academic success in postsecondary education, with prominent differences between rural and urban settings. Found, in the quantitative phase, were correlations, to different degrees, between high school involvement and postsecondary success. Qualitative data complements these findings by illuminating the factors that influenced the correlations between high school involvement and the challenges and successes encountered during the transition to postsecondary education. Implications for secondary and postsecondary instructors and advisors, as well as policymakers, are included.

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)0.002
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 score0.044
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.340
Teacher spread0.327 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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