MétaCan
Menu
Back to cohort
Record W1985102093 · doi:10.1115/esda2008-59045

Training Teams in Managing Projects in a Matrix Structure

2008· article· en· W1985102093 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsDebriefingProject managementComputer scienceProcess (computing)Knowledge managementWork breakdown structureProcess managementControl (management)Team managementEngineering managementProject management triangleOPM3EngineeringSystems engineeringMedical educationArtificial intelligence

Abstract

fetched live from OpenAlex

Projects are performed in different kinds of organizations: functional structure, project-based structure or matrix structure. The matrix organization is a combination of the functional organization and the “pure” project organization. In a matrix organization, there are usually two chains of command. The chain dealing with issues related to the functional division and the chain dealing with issues related to the project. Due to the split authority between project managers and functional managers, management becomes much more complicated. The cooperation between the project managers is vital for the matrix organization to perform well. Therefore, training teams of project managers in the matrix structure environment is required. A new method for training teams of project managers is presented. The proposed method is based on a real-time simulation called the Project Team Builder (PTB). PTB simulates a dynamic, stochastic multi-project management environment. A project management course for graduate students in systems engineering utilized PTB. The students used the simulator in a multi-user multi-project mode. A class of undergraduate engineering students participated in the same experiment as a control group. The 132 participants were divided into teams of three students (44 teams) which performed repetitive simulation-runs. Three factors were investigated: 1. Previous experience, 2. History recording mechanism, and 3. Team debriefing process. The findings indicate that for the initial learning phase, and for the transfer to different scenario phase, these three independent factors affect the performances. Furthermore, the interactions between the experience and history factors; between the experience and debriefing factors; and between the history and debriefing factors were all significant. Based on these findings a new paradigm for simulation-based team-learning model in a matrix organization structure is presented. The new model includes integration of history mechanism and debriefing procedure in the Kolb’s Team Learning Experience model.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.063
GPT teacher head0.283
Teacher spread0.219 · 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