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Record W179276555 · doi:10.22329/celt.v6i0.3773

24. Developing and Validating an Instrument for Student Ratings of Teaching

2013· article· en· W179276555 on OpenAlexaffvenue
Gary A. Hunt, Lyn Baldwin, Ernest Tsui, Les Matthews

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsLikert scaleSurvey researchPsychologySurvey instrumentMedical educationMathematics educationReliability (semiconductor)Point (geometry)Applied psychologyMedicineMathematics

Abstract

fetched live from OpenAlex

In May 2007, the Thompson Rivers University Faculty of Science established an ad hoc subcommittee to develop a new student ratings of teaching survey. The final survey, approved by the Faculty in February 2011, includes statements categorized in the dimensions of teaching shown in previous studies to be correlated with student achievement. The survey is learner-centred, discipline and pedagogically neutral, and includes only items that can be reasonably evaluated by students. The survey consists of 40 items including eight statements of student background information, 32 statements to rate on a six-point Likert scale, and four open-ended questions. We demonstrated that a faculty group with no formal training in survey design and informed by the literature, can, in collaboration with faculty, develop a survey established as having a high degree of inter-rater reliability.

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)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.082
GPT teacher head0.416
Teacher spread0.334 · 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 teacher head, not a consensus.

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
Published2013
Admission routes2
Has abstractyes

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