First-Year Civil Engineering Students’ Knowledge and Confidence in the Use of Visualization and Representation Tools to Solve Engineering Problems
Bibliographic record
Abstract
Abstract First-Year Civil Engineering Students’ Knowledge and Confidence in the Use of Visualization and Representation Tools to Solve Engineering Problems First-Year Civil Engineering students come to their programs with a diverse array of skills and motivations. Previous research by the authors indicates that students often choose engineering as a major due to guidance counselor and parental recommendations based on their performance in math and science courses in high school and the professional prospects afforded by an engineering degree. So, we might expect that students arrive in college relatively confident in their skills in mathematics, but perhaps less so when it comes to writing or the other skills necessary to succeed in engineering courses. Further, their knowledge of the engineering profession itself is often minimal. The authors have noticed in particular a marked resistance to engagement at any level to use visualization or representation to solve engineering problems. This introductory study evaluates a group of first-year civil engineering students at X University with regard to their attitudes about their current skills, the skills they see as most important for success, and the specific tools of visualization with which they have familiarity. Based on the results of a survey given to the students at the beginning of the semester, making sketches, diagrams, graphs, etc. and using them as tools to learn, to investigate, and to document are skills that students are not familiar or comfortable with when they enter the program. This paper will describe the results of that survey and the introduction of four specific assignments that are designed to both improve these skills and foster appreciation for these skills in the first-year engineering design course. The authors will evaluate the impact on the students’ perception of their abilities and their level of comfort in using these visualizations skills to solve engineering problems.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".