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Record W2992032345 · doi:10.1111/jcal.12398

Students' perceived usefulness of computerized percentage‐only vs. descriptive score Reports: Associations with motivation and grades

2019· article· en· W2992032345 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

VenueJournal of Computer Assisted Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDescriptive statisticsPsychologyDescriptive researchTest (biology)Mathematics educationMedical educationApplied psychologyStatisticsMedicine

Abstract

fetched live from OpenAlex

Abstract In computer‐based testing (CBT) environments instructors can provide students with feedback immediately. Commonly, instructors give students their percentage correct without additional descriptive feedback. Our objectives were (a) to compare students' perceived usefulness of a percentage‐only score report vs. a descriptive feedback report in a CBT environment and (b) to test relationships amongst perceived usefulness, motivation, and exam performance. Using a semester‐long repeated measures design embedded into three real examinations, we found that students perceived the descriptive feedback report as more useful than the percentage‐only report. However, there were no relationships amongst the usefulness of the score report and students' motivation or exam scores. Instead, previous performance was the strongest positive predictor of future performance. We discuss the effortful work required to create descriptive feedback reports with their utility and suggest ways instructors may better support students in using descriptive feedback reports when they are implemented.

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)0.003
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 score0.035
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.078
GPT teacher head0.355
Teacher spread0.277 · 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