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Record W2809770375 · doi:10.22329/celt.v11i0.4973

A study of the first-year academic experience at a growing liberal arts institution

2018· article· en· W2809770375 on OpenAlexaffvenue
Robyne Hanley-Dafoe, Cathy Bruce

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsTrent University
Fundersnot available
KeywordsSyllabusTransformative learningLiberal arts educationInstitutionHigher educationPedagogyStudent engagementPsychologyTeaching methodMathematics educationSociologyMedical educationSocial sciencePolitical scienceMedicine

Abstract

fetched live from OpenAlex

How can universities ensure that the first-year learning experiences for students are in alignment with a vision of education that is purposeful, personal and transformative? This essay presents the study of Trent University’s first- year academic experience, that aimed to uncover the problems post-secondary institutions face in the ever-changing landscape of first year teaching and learning. The study captured the perspectives of faculty, student support staff and students, in both first and second year, in relation to their academic experiences. The study led to the generation of a series of recommendations and wayforwardings for consideration with the broader goal of supporting student retention as well as quality teaching and learning experiences for both students and faculty. The study spanned 18 months and included surveys, reflections, and an environmental scan of 92 first year course syllabi. This essay also includes a condensed literature review pertaining to student transition theory, student engagement, student motivation to learning, student retention, and 21st century students.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
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 head0.036
GPT teacher head0.380
Teacher spread0.345 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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