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Record W4392681710 · doi:10.22318/icls2023.432296

Does the Emergency Switch Make a Difference in STEM Motivationally Supportive Instruction? Comparing Instructors’ Motivational Support Across Pre-Pandemic In-Person and During-Pandemic Online STEM Learning Environments

2023· article· en· W4392681710 on OpenAlexaff
Qiyuan Zheng, Kristy A. Robinson

Bibliographic record

VenueProceedings. · 2023
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsMcGill University
Fundersnot available
KeywordsEnthusiasmPandemicCoronavirus disease 2019 (COVID-19)PsychologyAutonomyRelevance (law)Medical educationMathematics educationMedicineSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Due to the global pandemic, universities experienced an emergency switch from inperson to remote teaching, which likely prompted changes in instructors' motivationallysupportive instruction.The present study investigated how instructors supported students' motivation through relevance statements, supporting autonomy, and showing their enthusiasm in pre-pandemic in-person and pandemic online STEM learning environments.Findings from lecture recording segments in 2019 and 2020 demonstrated instructors' in-class motivational support differed across the two learning conditions.

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)0.007
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.301
Teacher spread0.265 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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