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Record W1511655245 · doi:10.47678/cjhe.v39i1.491

Student Ratings of Teaching Effectiveness: Student Engagement and Course Characteristics

2010· article· en· W1511655245 on OpenAlex
Tanya Beran, Claudio Violato

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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Higher Education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAttendanceStudent engagementMathematics educationPsychologyPath analysis (statistics)Latent variableStatisticsMathematics

Abstract

fetched live from OpenAlex

Characteristics of university courses and student engagement were examined in relation to student ratings of instruction. The Universal Student Ratings of Instruction instrument was administered to students at the end of every course at a major Canadian university over a three-year period. Using a two-step analytic procedure, a latent variable path model was created. The model showed a moderate fit to the data (Comparative Fit Index = .88), converged in _0 iterations, with a standardized residual mean error of .03, χ2 (_49) = _988.59, p < .05. The model indicated that course characteristics such as status and description are not directly related to student ratings. Rather, they are mediated by student engagement, which is measured by student attendance and expected grade. It was concluded that, although the model is statistically adequate, many other factors determine how students rate their instructors.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.115
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.453
Teacher spread0.404 · 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