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Measuring Student Responses in and Instructors’ Perceptions of Student Evaluation Teaching (SETs), Pre and Post Intervention

2019· article· en· W3003628423 on OpenAlexaffvenue
Lisa Moralejo, Elizabeth Andersen, Norma Hilsmann, Lindsay A. Kennedy

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsThompson Rivers UniversityUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsSummative assessmentFeelingFormative assessmentIntervention (counseling)Class (philosophy)Set (abstract data type)PsychologyPerceptionMathematics educationReading (process)Medical educationPedagogySocial psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

At most colleges and universities, students are invited to complete Student Evaluation of Teaching (SETs), which have both formative and summative purposes. In this convergent mixed methods study we evaluated if we could influence (a) students’ numerical responses and nature of their comments and (b) instructors’ physical and emotional responses to SET results, their perceptions of their results, and perceptions of SETs overall. Students who received an in-class intervention submitted more qualified comments, addressed specific issues, and made more recommendations for improvements compared to students who did not receive the intervention. Instructors reported reduced physical symptoms related to SETs after they received the intervention. Instructors reported that the intervention helped them let go of feelings of frustration and isolation and that they had acquired new strategies for opening, reading, and interpreting SET results. They continued, however, to report feeling apprehensive, uneasy, and uncertain about impending SET results.

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)0.060
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0600.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.135
GPT teacher head0.453
Teacher spread0.318 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes2
Has abstractyes

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