Using Writing as a Learning Tool in Engineering Courses
Bibliographic record
Abstract
Successful engineers must be able to identify innovative approaches to solving real-world problems. For this reason, the ability to move beyond memorization and think critically is an essential skill for engineering students. Writing is an effective pedagogic tool for helping undergraduate students to develop such critical thinking skills (Bean, 2011). Moreover, research shows that short writing activities can help engineering students deepen their understanding of complex concepts so that they are prepared to engage more critically with course content (Kågesten & Engelbrecht, 2006; Welch Gradin & Sandell, 2002; Wheeler & McDonald, 2000). Yet despite the proven value of writing activities, they are not commonly used in engineering courses. In this workshop, participants explore the benefits and challenges of using writing activities in engineering courses at the undergraduate level. Participants are introduced to a number of writing activities that can easily be integrated into engineering courses, by either instructors or teaching assistants, in order to engage students in course material and encourage students to think critically about course concepts. By the end of this workshop, participants should feel comfortable using writing activities in their own teaching practice, as well as describing the learning benefits of using writing activities in engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".