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Record W2558731138 · doi:10.1109/fie.2016.7757615

Who wants to collaborate? A step towards understanding collaboration as choice

2016· article· en· W2558731138 on OpenAlexaff
Matthew Bojey, Bowen Hui

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClass (philosophy)Computer scienceProductivityOrder (exchange)Collaborative learningWorld Wide WebMathematics educationKnowledge managementPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Emphasis on 21stcentury skills has placed much importance on providing students with collaboration opportunities, despite the resistance by some students who prefer to work alone. In order to facilitate a flexible learning environment that fosters both individual productivity as well as collaborative problem solving, we designed a study to better understand the factors influencing students' choice to collaborate in an online setting. We developed a web-based learning software for practicing linked list exercises and conducted a study with 67 participants in a CS2 class. Our results indicate that online collaboration provides a peer learning opportunity for students with lower confidence to become more comfortable with the material. Moreover, we analyzed student data and report on the performance tradeoffs (speed vs. number of mistakes) between working collaboratively and working solo.

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)0.017
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 score0.018
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.024
Scholarly communication0.0180.030
Open science0.0020.006
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.103
GPT teacher head0.433
Teacher spread0.330 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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