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Record W3091251830 · doi:10.18260/1-2--34029

A Partnership Model for Integrating Technical Communication Habits Throughout Undergraduate Engineering Courses

2020· article· en· W3091251830 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venue2020 ASEE Virtual Annual Conference Content Access Proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsCourseworkGeneral partnershipContext (archaeology)Engineering educationMathematics educationComputer scienceMedical educationEngineeringPsychologyEngineering managementMedicine

Abstract

fetched live from OpenAlex

Abstract The ability to communicate well is often cited as one of the most valued skills for engineers in the workplace (ABET). The context for this research is a “partnership model” in which an embedded writing instructor collaborates with an engineering instructor to integrate writing instruction into engineering coursework. Reave (2004) characterized partnership as an authentic form of integration, in which faculty collaborate in designing and delivering instruction, and in assessing student outcomes. Thus, in the partnership model, students acquire technical communication habits by engaging in authentic forms of communication – the kind of writing typically required of engineers in the workplace, including laboratory reports. The descriptive study reported here investigates how the partnership model supports students in writing technical lab reports, and explores how this model might be used to systematically improve the writing skills of engineering students taking lab courses supported by both engineering and writing faculty. The sample was drawn from students in a junior level Chemical Engineering (CHME) laboratory course with 12 students. Both writing and engineering instructors team-taught lectures on technical writing and provided students’ ongoing feedback on their writing. Instructors collaborated with engineers from other engineering disciplines to develop resources for writing lab reports and writing exercises; these resources were available online and assigned to students in the CHME course. Data collected for analysis included statistics of students’ use of online writing resources, student satisfaction surveys, lab report grades, in-class writing exercises, and teaching artifacts as well as comparison data from student lab reports in previous courses. Results from the junior-level end-of course survey indicate that students perceived the process of writing a laboratory report more difficult than completing the scientific and engineering computations and analyses required for each report. The students in the CHME sample will be followed through the next course in the sequence and data collection, including a second end-of-course satisfaction survey, will continue through the Fall of 2019. The results of the analysis of students’ writing samples collected over time will be reported in the paper, including improvements in overall grades on the lab reports as well as areas of strengths and weaknesses in different components of the lab reports. Findings from this analysis will show how the students in the CHME partnership study applied what they learned from the instruction and resources that collaboration among instructors of different disciplines afforded them. This descriptive study of a partnership model is offered as one example of bringing new and critical perspectives to engineering education. References ABET. (n.d.) Criteria for Accrediting Engineering Programs, 2018 – 2019. Retrieved from https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-programs-2018-2019/ Reave, L. (2004). Technical communication instruction in engineering schools: A survey of top-ranked US and Canadian programs. Journal of Business and Technical Communication, 18(4), 452-490.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.300
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it