MétaCan
Menu
Back to cohort
Record W1928825815 · doi:10.22329/celt.v5i0.3423

12. Undergraduate Essay Writing: Online and Face-to-Face Peer Reviews

2012· article· en· W1928825815 on OpenAlexaffvenue
Mike Chong, Lori Goff, Kimberley J Dej

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPeer reviewPeer feedbackFace-to-facePeer evaluationFace (sociological concept)Computer sciencePsychologyMathematics educationOnline discussionPeer instructionWeb applicationMedical educationMultimediaWorld Wide WebHigher educationSociologyChemistryMedicineSocial science

Abstract

fetched live from OpenAlex

We implemented two different approaches of using peer review to support undergraduate essay assignments for students taking large second-year courses in life sciences and biology: a web-based online peer review (OPR) approach and a more traditional face-to-face peer review (FPR) approach that was conducted in tutorial settings. The essays consisted of a review of current literature to discuss the molecular involvement of cancer development or stem-cell growth. Following implementation of the peer reviews, we conducted a preliminary analysis of the pros and cons of using the two methods. Student and instructor feedback suggested that the activity of peer review was generally perceived as valuable regardless of which approach was used. OPR was convenient and saved time and resources relative to FPR, but the technical drawbacks using the OPR approach made it challenging for some students to use. A subsequent investigation using alternative OPR programs that offer additional functionality is planned.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.040
GPT teacher head0.363
Teacher spread0.323 · 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 designNot applicable
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

Citations4
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueCollected Essays on Learning and TeachingSame topicStudent Assessment and FeedbackFrench-language works237,207