Organizational learning and benchmarking in university technology courses
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze the management practices that contribute to single and double cycles of organizational learning in the vocational education of the Brazilian higher education technology courses (HETC), and to study the learning outcomes that result through the Brazilian Ministry of Education SINAES indicators. Design/methodology/approach It consisted in both participant observation and quantitative phases. The participant observation phase included a benchmarking activity at École de Technologie Supérieure (ÉTS) de Montréal, to analyze and delimitate their practices for the preparation of the second phase, test the hypotheses by means of modeling of structural equations. Findings The key practices that contribute to organizational learning in the Brazilian HETC were identified through a benchmark activity at ÉTS by using a quantitative research scheme of single cycles of organizational learning, and further in correspondence with the Brazilian criteria (SINAES-Ministry of Education). Research limitations/implications The extent of the sample is concentrated in the southern region of Brazil (Rio Grande do Sul, Santa Catarina and Paraná), limiting its representativeness to a regional basis. Practical implications Practices that contribute to organizational learning are a counterpoint and a complement of the Brazilian Ministry of Education SINAES indicators, which value the formalization of those courses and the future actions in the Brazilian universities. Social implications The study re-inforces the importance of organizational learning for the development of excellence in Brazilian HETC. Originality/value The results contribute to build analysis frameworks on the relationships between management practices, organizational learning, benchmarking and organizational outcomes, particularly in the management of the technology courses and for Brazilian universities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".