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Fostering A Teaching And Learning Opportunity: Toward Equity In Student Feedback Of Teaching

2024· article· en· W4400405189 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.

Bibliographic record

VenuePapers on postsecondary learning and teaching. · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsBachelorEquity (law)AccountabilityMathematics educationProcess (computing)PsychologyCourse (navigation)Medical educationReflection (computer programming)Course evaluationTeaching methodHigher educationPedagogyComputer scienceMedicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Educators within post-secondary institutions receive input in the form of course evaluations from their students. The aim of receiving student input is to improve the teaching and learning experience for all. There are, however, inherent problems with the current methods of obtaining students' views through course evaluations. In this pilot study, the researchers focused on two problems: universally low response rates of 20% or less of student input in formal course evaluations and the problematic bias associated with anonymous course evaluations. Implementing practices that encourage students to provide course feedback, thus moving away from the term course evaluation was a first step to address these problems. A process was piloted in this study with 16 domestic undergraduate Bachelor of Science students whereby the researchers encouraged reflection, dialogue, and accountability in the new model and compared the differences against the problematic original model of receiving course evaluation input from students.

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)0.023
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.007
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 head0.116
GPT teacher head0.453
Teacher spread0.336 · 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