THE INFLUENCE OF PERSONALITY TYPE ON TEAMWORK IN ENGINEERING EDUCATION
Bibliographic record
Abstract
The influence of personality type on various factors relating to engineering education is examined. Personality type was described according to the Myers-Briggs Type Indicator (MBTI). Data from a total of sevencohorts (2007 to 2013) in a second year mechanical engineering design course have been analyzed. Decision making on team tests was examined in terms of the MBTI Introversion / Extraversion domain and peer evaluation scores received were examined across all four MBTI domains. Measured differences between students with a preference for Introversion and those with a preference for Extraversion on the level of influence on team decision making was found. A small but statistically significant correlation has also been noted between peer evaluation scores received and a student’s preference onthe MBTI Judging / Perceiving domain. This is believed to relate to possible perceptions (or misperceptions) that in delaying action or decision making a person with a preference for Perceiving is lazy or disengaged. Differences in peer evaluation scores were not observed for the other three MBTI domains. The results suggest that even with student awareness (through readings) and interventions (through workshops) possible effects of the difference in personality type persist in engineering student teams. The lack of relationship between peer evaluation scores and the remaining three MBTI domains is a favourable outcome in terms of the objectivity of the peer evaluation tools.
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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 | 0.002 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".