UNDERGRADUATE WRITING ASSIGNMENTS IN ENGINEERING: TARGETING COMMUNICATION SKILLS (Attribute 7)
Bibliographic record
Abstract
This paper will report on some of our findings from a national study investigating the writing demands placed on students in various disciplines, including Engineering. This is a timely study given that Reave notes that a “well-‐designed program [in Engineering] will include a solid foundation in communication skills,” something she says also requires high quality feedback. For our part of the study, we investigated which courses in our Engineering school target Attribute 7 (A7, Communication Skills) and then analyzed the course syllabi to determine whether they required written assignments. We then described these assignments according to 20 variables, such as the total number of assignments written per year, feedback provided and genre.Ever since the accreditation board introduced them, the graduate attributes and their assessment have become the focus of most Engineering schools, so much so that Engineering course syllabi will necessarily include both a series of course outcomes and a complex chart of the expected competency levels. However, information on the assignments themselves can be far less detailed. Consequently, our findings tend to be more suggestive than definitive, though certain trends do stand out. For example, while many writing scholars, such as Paretti and Reave, would argue that students should learn the various discipline-‐specific writing genres and then be able to shape their material to satisfy the specific rhetorical demands, many course syllabi in our study simply listed “assignments” rather than specifying the kind of assignment: Civil Engineering listed 16 “assignments” of 33 (total) and Mechanical Engineering 47 of 105.Finally, even though this paucity of detail may reflect what Broadhead calls the general “paucity of requirements for writing instruction” in an Engineering school, one of the goals of the national study was to initiate discussions about the way writing is taught and supported within the departments of the schools involved. Our study’s findings, suggestive as they are, may be able to initiate that discussion.
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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 | 0.005 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".