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Record W2170368393 · doi:10.24908/pceea.v0i0.4915

A TRAINING DEMONSTRATION FOR EXPERIENTIAL LEARNING IN OPC BASED PROCESS AUTOMATION DATA ACCESS

2013· article· en· W2170368393 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsMohawk College
Fundersnot available
KeywordsExperiential learningComputer scienceProcess (computing)AutomationKnowledge managementEngineering managementHuman–computer interactionEngineeringPsychologyMathematics education

Abstract

fetched live from OpenAlex

The focus of university classrooms is changing from a lecturing approach to a learning-by- doing approach. Consequently, many universities are moving towards incorporating hands-on or experiential learning approaches in their teaching methods. In this paper we present a training demonstration that we have developed to support experiential learning in the teaching of Open Process Control (OPC) based access to process automation data. This is a topic in the Advanced Components and System Integration course of the joint McMaster University and Mohawk College bachelor of technology program. Generally, the paper presents the design and implementation of an OPC data access system, and how the system is used as demonstration to inspire learning.

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)0.001
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 score0.059
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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