Active Learning for Creating Innovators: Employability Skills beyond Industrial Needs
Bibliographic record
Abstract
In 2012, Japan’s Ministry of Education, Culture, Sports, Science, and Technology initiated a project entitled “Improving Higher Education for Industrial Needs” in which 147 universities have participated. One of the main purposes of this project is to identify what industrial needs and help develop university students’ employability skills through active learning as there has been a growing concern that university graduates lack employability skills that industry seeks. However, Japanese university instructors are unfamiliar with or lack skills for adopting active learning approaches in their courses. This study explores what skills industry needs, examines how these skills can be developed and assessed at university, and describes a course entitled “Business Planning in Practice” that intends to develop these skills through active learning offered at the Nagoya University of Commerce and Business, one of the universities participating in the national project. Business Planning in Practice has been selected as an active learning target course for the Improving Higher Education for Industrial Needs to demonstrate how a university course can employ active learning approaches. The findings show that the skills that industry needs are discovery skills, these skills could be nurtured in the form of research skills at university, and Business Planning in Practice had positive impacts in improving these skills through active learning approaches. This paper concludes with suggestions for how Japanese universities could establish an environment to create innovative human resources. financial markets (equity, fixed income and derivative) are closely correlated with each other. However, we do see some level of lead-lag relationships among our variables and thus provide certain evidences against the efficient market hypothesis. Our results will offer insights towards a better understanding about how quickly different security markets process and reflect information thus benefit investors who wish to profit from the arbitrage opportunities.
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.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".