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Awareness, Use, and Value of Student Support Programs Through the Lens of Science Students, Professors, and Staff

2024· article· en· W4403603219 on OpenAlexaffvenue
A. Dana Ménard, Sira Jaffri, Kendall Soucie, Dora Cavallo‐Medved, Chris Houser

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsUniversity of WaterlooUniversity of Windsor
Fundersnot available
KeywordsValue (mathematics)PsychologyMathematics educationLens (geology)PedagogyMedical educationComputer sciencePhysicsMedicineOptics

Abstract

fetched live from OpenAlex

Science students face specific challenges associated with their field of study. The purpose of this study was to assess science students’ use of support services and programs, identify barriers to use and group differences, determine professors’ and staff’s familiarity with programs and solicit ideas from all participants about what programming changes should be made. Survey questions were completed by 308 students and 40 staff and professors in our institution’s Faculty of Science. Students’ participation rates and professors’ and staff’s familiarity with programs ranged significantly but most services were rated as helpful by both groups. Few demographic group differences emerged in program use. Participants recommended a number of improvements to the Faculty of Science including strengthening mental health awareness and support services, fostering student engagement in science, building students’ relationships with professors and cultivating a healthy learning environment. Implications for program development in science faculties are considered.

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)0.011
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.002
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 head0.135
GPT teacher head0.369
Teacher spread0.234 · 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 designQualitative
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
Published2024
Admission routes2
Has abstractyes

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Same venueThe Canadian Journal for the Scholarship of Teaching and LearningSame topicDiverse Educational Innovations StudiesFrench-language works237,207