Towards a Transformative Collaboration: Technical Writing, Engineering, Industry
Bibliographic record
Abstract
Abstract This paper reports on a work in progress collaboration between Engineering and English faculty at Texas A&M Qatar, and examines the initial impact of the team's reorganization of a required Technical Professional Writing course on engineering students' educational experience as they learn effective and relevant professional communication skills in the field of engineering; as they network with mentors from various industries; and as they train to be effective writers and competitive candidates in their engineering fields. While many engineering programs require several technical writing courses, our undergraduate engineering students at Texas A&M University Qatar take only ONE course in Technical Professional Communication (ENGL 210), followed by a selection of WI courses spread out in their degree plan. However, faculty still identified a continuing gap between their expectations of students' communication skills and what students have demonstrated in classes, including upper-division and capstone courses. How can we ensure the continuity of certain writing tools and skills throughout the student's writing journey at TAMUQ? How can we present opportunities for English and engineering faculty to share their assessments, rubrics, or other teaching tools so as to effectively support the student's communication development? Our research group is comprised of two Engineering faculty and two English faculty (one of whom is also a Writing in the Disciplines Coordinator – WID). Through extensive meetings and discussions, sharing of assignments and students' work, and review of course objectives and assessments, our team redesigned the required ENGL 210 to focus on what engineering faculty identify as relevant aspects of writing in the field of engineering: problem statements, research and literature reviews, project proposals, progress reports, and scientific poster design, with additional focus on integrating UX design and data visualization in the students' projects. We created assessments that more closely align with engineering faculty and industry professionals' expectations for effective oral and written communication. We also brought industry professionals to campus to discuss topics such as the importance of clear communication in engineering workplaces and effective team collaboration. We hypothesize that the significant collaboration between English and Engineering faculty in developing assignments, providing feedback to students throughout their projects, and assessing students' final products, as well as the partnership with various partner industries, considerably improves our students' writing journey at TAMUQ as they learn effective and relevant professional communication skills in the field of engineering. We also discuss the steps forward to make this collaboration a model for other courses in our curriculum at our institution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".