Individual Innovation Competence: A Systematic Review and Future Research Agenda
Bibliographic record
Abstract
Learning for innovation is a central element in European policymaking in developing higher education. Students often learn in project settings together with work organizations developing new solutions, products and services. These authentic creative, social and collaborative settings offer an attractive learning environment. The aim of this study was to determine the factors involved in individual innovation competence to be able to design, tutor and assess the pedagogical processes where authentic open-ended tasks are being solved transforming novel ideas into usable products or services. After defining the extraction criteria using a limited sample of articles, a bias-assessed systematic review was conducted of empirical research articles published in 2006–2015. Twenty-eight journal articles were ultimately included in the review. Despite the volume of academic literature in this field, comparatively few studies providing findings addressing the review objectives could be found. There was, however a reasonable weight of research evidence to support the result. The findings suggest that personal characteristics, such as flexibility, achievement orientation, motivation and engagement, self-esteem and self-management, future orientation, creative thinking skills, social skills, project management skills, and content knowledge and making skills can be needed in collaborative innovation process. These findings have implications for pedagogical innovation processes and for competency-based assessment.
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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 | 0.027 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".