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Record W4205814403 · doi:10.21833/ijaas.2022.01.015

Investigation for utilization of training resources in technical education: A comparative study

2022· article· en· W4205814403 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of ADVANCED AND APPLIED SCIENCES · 2022
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)StatisticsMathematics educationTest (biology)Computer scienceClass (philosophy)Variance (accounting)Sample (material)Limited resourcesTraining (meteorology)Sample size determinationStatistical analysisMathematicsArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

This research study presents a comparative study between the quarter and the semester systems in the technical institutes, in terms of scheduling, training, and utilizing the training resources such as classrooms/halls capacity and employing the instructors. The size of the study sample was represented by the total number of students in classrooms/halls for the study courses in the quarter system by 8836 students distributed over 363 sections. While in the semester system 10360 students distributed over 358 sections. Thus, a comparison was made based on one training year between the two training systems for basic skills courses. The samples were used to know the effect of class capacity and teaching loads on the training system by making initial comparisons, and statistical tools were used where averages of class capacity and teaching loads were calculated to know the status and trends of the data using the plot box. In addition to descriptive statistics (Two samples F-test for variance) and finally, (t-test: Two samples assuming unequal variance) were selected. The p-value less than 0.05 of single-tailed confirmed that classroom capacity and instructors’ load were higher in the semester system compared to the quarter system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.179

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 head0.055
GPT teacher head0.339
Teacher spread0.283 · 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