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Record W2124140652 · doi:10.2308/iace-50754

The Power of Giving Feedback: Outcomes from Implementing an Online Peer Assessment System

2014· article· en· W2124140652 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

VenueIssues in Accounting Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPeer feedbackPeer assessmentPeer reviewWork (physics)Quality (philosophy)PsychologyPeer-to-peerPeer evaluationComputer scienceMedical educationAccountingMathematics educationHigher educationPolitical scienceWorld Wide WebBusiness

Abstract

fetched live from OpenAlex

ABSTRACT This paper describes an online system that facilitates peer assessment of students' course work and then uses data from individual case writing assignments in introductory financial accounting to empirically examine associations between peer assessment and case writing performance. Through this description and empirical analysis, the paper addresses the following questions: (1) Why use peer assessment? (2) How does online peer assessment work? (3) Is student peer assessment reliable? (4) What do students think of peer assessment? (5) Does student peer assessment contribute to academic performance? Three key findings from this study are that students at the sophomore level were able to generate reasonably reliable feedback for peers, they valued the experiences involved in providing peer feedback, and giving quality feedback had a more significant and enduring impact on students' accounting case analyses than did receiving quality feedback, after controlling for differences in accounting knowledge and case writing skills.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.184
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.414
Teacher spread0.384 · 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