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Record W2071016044 · doi:10.1080/02602930500260688

Ratings of university teacher instruction: how much do student and course characteristics really matter?

2005· article· en· W2071016044 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAssessment & Evaluation in Higher Education · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyMathematics educationClass (philosophy)Higher educationStudent teacherMedical educationTeacher educationMedicineComputer science

Abstract

fetched live from OpenAlex

Abstract Several student and course characteristics were examined in relation to student ratings of instruction. Students at a major Canadian university completed the Universal Student Ratings of Instruction instrument at the end of every course over a three‐year period, providing 371,131 student ratings. Analyses of between‐group differences indicate that students who attend class often and expect high grades provide high ratings of their instructors (p < .001). In addition, lab‐type courses receive higher ratings than lectures or tutorials, and courses in the social sciences receive higher ratings than courses in the natural sciences (p < .001). Regression analyses indicated, however, that student and course characteristics explain little variance in student ratings of their instructors (<7%). It is concluded that student ratings are more related to teaching instruction and behavior of the instructor than to these variables.

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.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.084
GPT teacher head0.466
Teacher spread0.382 · 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