Opportunities for Adaptive Learning Environments to Promote Sustainability-Oriented Innovation Competence in Vocational Education and Training
Bibliographic record
Abstract
Achieving a sustainable economic system is a key challenge facing society. However, sustainable business to date has been only minimally considered when it comes to the requisites and curricula of business trainees. It generally has been left up to schools and teachers to provide their students with sustainable business skills. This involves creating teaching and training that effectively harmonize with learner requirements. To support teachers in this process, the following develops a sustainability-oriented innovation competence typology using a latent profile analysis based on data gathered from 1,149 business trainees who were in the first, second, or third year of their apprenticeship. This typology can be used to plan and develop classroom teaching. Competency assessment was done using a multiple-choice test along with a questionnaire to determine students’ beliefs about sustainable development. The latent profile analysis revealed six groups of learner competence profiles, each of which require specific teaching when it comes to achieving sustainable innovation skills. Based on these, the following paper develops recommendations for specific teaching methods and lessons that effectively promote business trainee sustainability-oriented innovation competence, while at the same time including their specific requirements into teaching.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".