Learning Style Differences between Undergraduates, MBAs, Nonmanagement Workers, and Managers in Japan
Bibliographic record
Abstract
Using Kolb’s experiential learning theory, this study aimed to explore how learning style differed among four cohorts: undergraduate management majors, master’s of business administration (MBA) students, nonmanagement workers, and managers. The research context was Japan, with 1080 participants from two universities, one business school, and two different firms focused on sales and production. To compare the four cohors, this study applied a cross-sectional study. Results indicated that managers showed the strongest preference for active over reflective learning, followed by MBA students, nonmanagement workers, and undergraduates. Managers’ active learning orientation did not statistically differ from that of MBA students, but did statistically differ from the groups of nonmanagement employees and undergraduates. With regard to the learning dimension of thinking versus feeling, MBA students were the most abstract learners, while the other three groups exhibited concrete learning orientations. The present research was the first to empirically compare groups according to career transitions from undergraduates towards management positions. This study provides insight on how individuals differ in learning styles as their careers develop.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".