MétaCan
Menu
Back to cohort
Record W2053187089 · doi:10.18260/1-2--8497

Internet Based Interactive Construction Management Learning System

2020· article· en· W2053187089 on OpenAlexfundno aff
Jeremy Koczenasz, Bradley Bashford, Anil Sawhney, André Mund

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersArizona State UniversityWestern Michigan UniversityUniversity of AlbertaNational Science Foundation
KeywordsComputer scienceThe InternetSession (web analytics)VRMLCurriculumMentorshipMultimediaConstruction managementEngineering managementWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

The Del E. Webb School of Construction is currently involved in a three-year project aimed at enhancing the construction management education.The primary undertaking of this project-in its second year-is to incorporate practical content in the construction curricula thus bridging the gap between the classroom and the construction site.Enhancements are being accomplished by developing 1) an Internet-based Interactive Construction Management Learning System (ICMLS) and 2) an advising and mentorship program that will enhance practitioner-involvement.The Interactive Learning System uses interactive and adaptive learning environments to train students in the areas of construction methods, equipment and processes.This system is being developed using multimedia; Internet based computing; Virtual Reality Modeling Language (VRML); databases; and discrete-event simulation.This paper will provide an update on the design, development and implementation of ICMLS.Lessons learned and tools utilized that may be helpful in other branches of engineering and non-engineering fields will be described.The project team envisions that the successful completion of this project will lead to a number of benefits including: 1) improved recruitment, retention, and program completion for the construction management program; 2) "jobready" graduates that can be successfully employed in the construction industry; and 3) enhanced practitioner involvement and construction industry input. Introduction and BackgroundPreparing students for the challenges of managing large construction projects is an important responsibility and a difficult task 15 .The instruction methods used in the majority of construction engineering and management curricula rely, for the most part, on traditional methods such as exposing students to applied science courses.These traditional teaching methods, however, are often not fully adequate in providing students with all the skills necessary to solve the real world problems Page 5.396.1

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0750.021

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 head0.008
GPT teacher head0.178
Teacher spread0.170 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations42
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicBIM and Construction IntegrationFrench-language works237,207