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Record W1940128419 · doi:10.19173/irrodl.v14i5.1524

Peer feedback to facilitate project-based learning in an online environment

2013· article· en· W1940128419 on OpenAlexvenueno aff
Yu‐Hui Ching, Yu‐Chang Hsu

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2013
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPeer feedbackPeer learningPerceptionComputer-mediated communicationPeer assessmentLearning environmentPeer reviewEducational technologyPsychologyComputer scienceOnline learningCooperative learningPeer evaluationActive learning (machine learning)Higher educationMathematics educationMultimediaTeaching methodWorld Wide WebThe Internet

Abstract

fetched live from OpenAlex

There has been limited research examining the pedagogical benefits of peer feedback for facilitating project-based learning in an online environment. Using a mixed method approach, this paper examines graduate students’ participation and perceptions of peer feedback activity that supports project-based learning in an online instructional design course. Our findings indicate that peer feedback can be implemented in an online learning environment to effectively support project-based learning. Students actively participated in the peer feedback activity and responded positively about how the peer feedback activity facilitated their project-based learning experiences. The results of content analysis exploring the peer feedback reveal that learners were mostly supportive of peers’ work and they frequently asked questions to help advance their peers’ thinking. The implications and challenges of implementing peer feedback activity in an online learning environment are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.049
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.336
GPT teacher head0.520
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations65
Published2013
Admission routes1
Has abstractyes

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