Exploration of Engineering Students’ Values with Respect to Behaviors in Group Work
Bibliographic record
Abstract
In order to train young professionals, instructional methodologies in engineering need not only teach students knowledge, but must also instill the values and teach the behaviors— competencies students can demonstrate—required of professional practice. Herein, we focus on understanding the values and behaviors of students with respect to working as a member of an engineering group as a part of a course project. Our hypotheses are (1) that the students’ values with respect to the behavior of individuals in a group will remain stable through the academic year and (2) there will be behavioral predictors to group-based values. Our findings agree with the literature on societal groups which indicate that values should remain constant over time; we see here with our cohort of students that values not only remain stable, but also, students maintain high agreement through the academic year. With respect to behavior predictors, the behaviors that repeatedly correlated or predicted positive group values were related to interpersonal skills rather than knowledge or learning. This finding is important as it points to a noted necessity to foster strong interpersonal skills among students. Students need to recognize that how they interact with their group is just as important as the skills being brought to the group. The results presented herein are a first step toward creating a “personalized” instructional approach that focuses on aligning individual values and behaviors when working in an engineering group.
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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 | 0.004 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".