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Record W4323321614 · doi:10.5204/ssj.2588

Academic-Support Environment Impacts Learner Affect in Higher Education

2023· article· en· W4323321614 on OpenAlexafffund
Lindsey E. Voisin, Casey Phillips, Veronica M. Afonso

Bibliographic record

VenueStudent Success · 2023
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsNipissing University
FundersNipissing University
KeywordsAnxietyAffect (linguistics)PsychologySession (web analytics)PerceptionIntervention (counseling)Learning environmentClinical psychologyMedical educationApplied psychologyDevelopmental psychologyMathematics educationMedicineComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Unlike university classrooms, academic support services provide students opportunities for enactive mastery of a skill with immediate feedback in a low-risk learning environment. Given that this environment likely alters affective states, this study tracked support-seekers’ perception (n=107) of their anxiety and confidence before and after repeated 1 hour academic skill development sessions (n=384). Results showed that academic-support environment had a robust, immediate, and long-lasting effect on decreasing anxiety and increasing confidence. Positive outcomes such as reduced anxiety and increased confidence during an academic skill development session were associated with increased academic performance. There was a high rate of participants (98%) persisting into the next year of their program. Together this study demonstrates that the academic-support environment can provide intervention in the form of enhancing affective states in situations of high anxiety and low confidence to potentially affect academic outcomes and retention rates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.094
GPT teacher head0.473
Teacher spread0.378 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2023
Admission routes2
Has abstractyes

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