SELF-PERCEPTION DIFFERENCES BASED ON GENDER AND PERSONALITY TYPE IN TEAM PROJECTS
Bibliographic record
Abstract
Factors affecting student self-perception in thecontext of engineering design team work are examined inthis paper. Specifically, how students of different genderand personality type rank their own contributions to theirteam relative to how their teammates rank theircontributions is considered. Gender- and personalitybaseddifferences in self-serving bias – an individual’stendency to attribute positive outcomes to their ownactions and negative outcomes to external factors – areknown to exist.This two-part study examines these factors in thecontext of self-evaluation and peer evaluation scoresreceived in a second year mechanical engineering designproject course. Four evaluation events were conducted inJanuary, 2015 (Part 1), and followed by an in-classintervention (presentation) and three more evaluationevents in April, 2015 (Part 2). In Part 1, self-servingbiases were measured by examining the differencebetween self-evaluation scores and average peerevaluation scores received from teammates. Separate ttestsand mixed-linear model statistical analysis wereused to compare the average self-peer bias in evaluationscores versus gender and each of the four MBTI domainscales.Data showed a statistically significant increase in selfservingbias as the course progressed. Differences werealso noted for gender (males initially had a higher selfservingbias than females, but this difference disappearedin time), and MBTI domains of Introversion/Extraversionand Thinking/Feeling (students with a preference forExtraversion and Thinking had higher self-serving bias).The differences for gender and personality type werestatistically significant with t-tests but not with mixedlinearmodels, suggesting the observed effects were drivenby a small number of individuals with large self-servingbias. Following the intervention – consisting of a shortin-class presentation describing the observed effects fromPart 1 – reduced self-serving bias was observed in Part 2,but it is unclear if the change was due to the interventionor due to other factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".