Real World Projects with Companies Supporting Competence Development in Higher Education
Bibliographic record
Abstract
The department of business administration of Münster University of Applied Sciences (MUAS) in Germany has a long tradition in realising practice-oriented research projects in cooperation with industry. The objective of these cooperative projects is to offer students real-life experiences and to make the theoretical know-how of university lectures more tangible by using it in an actual business case setting. Students are given responsibility for project deliveries fitting the expectations of real companies in their real business. Through the projects students are encouraged to develop individual learning and problem solving competencies. In this paper, four good practice examples for university-industry cooperation integrated in the education of students in the field of marketing, specifically market analysis, will be presented. The project descriptions will highlight the different methodological approaches, focusing on their specific innovative features. The paper will evaluate the competencies students gain during their involvement in those kind of projects. To follow a valid scientific approach, the competence matrix of Erpenbeck & Heyse will be presented and used to highlight the specific competences gained by the students working on those projects.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".