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Record W4366158460 · doi:10.1016/j.ssaho.2023.100525

Exploring students' willingness to provide feedback: A mixed methods research on end-of-term student evaluations of teaching

2023· article· en· W4366158460 on OpenAlexaff
Luis Francisco Vargas‐Madriz, Norma Nocente

Bibliographic record

VenueSocial Sciences & Humanities Open · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of AlbertaMcGill University
Fundersnot available
KeywordsSummative assessmentFormative assessmentSet (abstract data type)InterviewProcess (computing)Quality (philosophy)PsychologyPerceptionMathematics educationMedical educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

Student Evaluations of Teaching (SET) are one of the most consistently administered tools to assess teaching performance in higher education institutions. SET affect the careers of individuals (summative evaluation), and have potential to shape the quality of instruction (formative evaluation). Past studies have addressed several issues with SET, but few have focused on surveying and interviewing students to better understand how they navigate and complete these evaluations. Therefore, a mixed methods design was used to explore university students' willingness to provide feedback through SET as part of the teaching evaluation process. Results indicate students’ positive views about the evaluation process and their perception of usefulness of evaluations increased their willingness to provide feedback, whereas potential student biases decreased their willingness to provide feedback. More importantly, results also highlight students are still not aware of, and do not really understand, the implications of their SET responses.

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)0.084
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0840.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.002
Scholarly communication0.0010.002
Open science0.0040.002
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 head0.841
GPT teacher head0.697
Teacher spread0.143 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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