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Record W3028174053 · doi:10.37590/able.v41.abs78

Examining the efficacy of peer feedback as part of the writing process in an introductory biology course

2020· article· en· W3028174053 on OpenAlexaff
Laurie Pacarynuk, Jennifer Burke

Bibliographic record

VenueAdvances in Biology Laboratory Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsCourse (navigation)Peer feedbackWriting processProcess (computing)Mathematics educationComputer sciencePsychologyPhysicsProgramming languageAstronomy

Abstract

fetched live from OpenAlex

A goal of introductory biology labs is to introduce students to scientific writing by having them write lab reports. With several lab sections taught by different instructors, consistency in grading is a problem. Another challenge is that students do not appear to engage with instructor feedback; they see writing as subjective, or fail to use the feedback to improve. One approach to these problems is having students carry out peer assessment with the goals of: encouraging engagement in the process of writing, introducing students to the peer review process (fundamental to science), and addressing inconsistencies in grading. Students in Biology 1010 used Moodle Workshop to grade exemplar Introductions and Discussions. They then prepared and peer evaluated Introductions and Discussions. Students were surveyed (22% responding: 44/200). Forty-five percent of respondents disagreed with the statement: I feel that the peer-feedback that I received helped to improve my writing; however, 90% of these identified stress and/or the belief that they would have received higher marks from their instructors as the reason(s) behind their negative evaluations. Eighty-six percent agreed with the statement: I feel that engaging in the peer-feedback process

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)0.001
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 score0.172
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.412
Teacher spread0.383 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2020
Admission routes1
Has abstractyes

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