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Record W3181479599 · doi:10.24908/pceea.vi0.14867

REMOTE LEARNING IMPACTS ON STUDENT WELLBEING

2021· article· en· W3181479599 on OpenAlexafffundvenueabout
Peter Ostafichuk, Mun Yee Mimi Tse, Jacob Power, Carol P. Jaeger, Jonathan Nakane

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of British Columbia
FundersUniversity of Prince Edward Island
KeywordsCohortTracking (education)Mental healthPsychologyAcademic yearMedical educationScale (ratio)StressorDemographyMedicineGeographyMathematics educationClinical psychologyPedagogySociologyPsychiatryCartography

Abstract

fetched live from OpenAlex

This study tracks wellbeing of a large cohort of first-year engineering students at a large Canadian university over a remotely-delivered academic year. This continues a similar tracking study completed with inperson instruction the previous year. Data were collected through short, weekly surveys rotating through the student cohort. Overall, the results show relatively consistent stressors across the year, driven primarily by academics, and very similar to data from the previous year delivered in person. Wellbeing scores (measured through the Warwick-Edinburgh Mental Wellbeing Scale) declined slightly through the year, and were overall lower than the previous academic year delivered in-person. Considering factors of EDI, female students showed slightly lower wellbeing scores than male students, while international students showed slightly higher scores than Canadian 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 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 score0.364
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.325
Teacher spread0.310 · 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

Citations2
Published2021
Admission routes4
Has abstractyes

Explore more

Same venueProceedings of the Canadian Engineering Education Association (CEEA)→Same topicCOVID-19 and Mental Health→French-language works237,207→