SELF PERCEPTIONS OF STUDENTS’ HUMANITIES-BASED SKILLS IN AN ENGINEERING PROGRAM: OVERVIEW OF A LONGITUDINAL STUDY’S FIRST TWO YEARS (2017-2019)
Bibliographic record
Abstract
This paper is a continuation of research from a previous paper presented to CEEA on a three-year longitudinal study aimed at assessing engineering accreditation non-technical skills at a medium sized engineering school at a large research university. The goal of this longitudinal study is to improve the assessment of these non-technical graduate attributes and test a metric to do so. The Likert-style survey focuses on engineering students self-perceptions of teamwork, communication skills, engineering ethics, professionalism, and lifelong learning in order to gather quantitative data that can be analyzed for trends. Self-perceptions are the focus of this study because student self-efficacy has been found to be correlated with student success over the long term. The study has been conducted through pre-and post-surveys testing whether engineering students’ self-assessment of their abilities in those areas increased or decreased from year to year. Currently, the longitudinal study has only just completed data collection for its final year of the three-year study, so the focus of this paper will be adding the results of the second year to the first, which were presented to CEEA last year. This paper analyzes the data gathered in the second year of the longitudinal study and continue the analysis of those results to explore what they can offer to our understanding of non-technical engineering graduate attributes. These findings are not meant to replace other initiatives, but to offer another metric to examine the effectiveness of engineering programs and meeting non-technical accreditation requirements.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".