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A project management perspective on student's declarative commitments to goals established within asynchronous communication

2009· article· en· W1601431041 on OpenAlexaff
François Chiocchio, Alexandre Lafrenière

Bibliographic record

VenueJournal of Computer Assisted Learning · 2009
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCommitTeamworkPerspective (graphical)Asynchronous communicationTask (project management)Knowledge managementProject managementComputer sciencePsychologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

Abstract Teamwork and technology, even as people are seeing their increased use in organizations, are becoming important components of problem‐based learning in academic settings. Yet, fostering computer‐assisted teamwork is complex and time consuming. Knowing how and when to intervene would prove useful. This study draws from the field of project management to explore how students commit to project goals using collective asynchronous text‐based communication technology. Declarative commitments – goal‐orientated public, voluntary, explicit and non‐retractable messages comprised of a term, an objective and a focus – made by 34 teams during a four‐phase 13‐week project were analysed qualitatively and quantitatively. Qualitative results show that declarative commitments voluntarily and formally package information about project constraints into a relatively potent message about tasks, coordination and project completion. Team members' suggestions as to what should be carried out in the project and requests for help often preceded others' declarative commitments. As with persuasive communication (i.e. aimed at changing beliefs, attitudes and behaviours), declarative commitments were followed by demands for clarification, new declarative commitments, confirmations of upheld commitments and clear approvals of what was committed to. Looking at project progression from a broader perspective, quantitative analyses show that declarative commitments did partially mediate the relationship between frequencies of task issues and of task solutions. This was particularly pronounced in the mid‐point of the project, but it was not the case during the initial or final phases of the project. Taken together, these results suggest that teachers can facilitate computer‐assisted learning and project goal attainment by monitoring asynchronous electronic discussions, and by eliciting and structuring declarative commitments.

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)0.011
metaresearch head score (Gemma)0.028
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 score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.009
Scholarly communication0.0090.005
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.366
Teacher spread0.342 · 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

Citations11
Published2009
Admission routes1
Has abstractyes

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