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Record W3196611878 · doi:10.18260/1-2--38095

WIP: Short Online Films to Help First-Year Students Write Reports as Engineers

2024· article· en· W3196611878 on OpenAlex
Michael Alley, Kaitlyn Pigeon, Stephanie Cutler

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

Venue2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsComputer scienceMultimediaMathematics educationPsychology

Abstract

fetched live from OpenAlex

Abstract Because many engineering students do not take a technical writing course until their junior or senior year [1], a gap exists between the essays that students have learned to write in first-year composition and the reports that those students are expected to produce in many undergraduate design courses and laboratory courses. This paper introduces a series of ten online films (3 – 7 minutes each) to help undergraduates write engineering reports [2]. Since the release of this series at the beginning of 2020, these films have received a combined 8500 film views. Created using the NSF approach of I-Corps™ Learning [3], the films have derived their content from one-on-one interviews with more than 100 engineering students and more than 25 engineering faculty. The focus of these interviews was to understand the gap between what undergraduates already knew about writing from first-year composition and what is needed to write an engineering report. Over three semesters, we piloted the films to hundreds of students in first-year seminars and at the beginning of engineering writing courses. From these pilot tests, we gathered information about the film series which we incorporated into the 2020 version. Although a technical writing course in the junior or senior year should bridge the discussed gap, not understanding the differences between general writing and engineering writing poses problems for engineering undergraduates. For instance, not recognizing what first-year design instructors expect in a summary can pull down a report's grade and lead students to assume that they are inherently not good at engineering writing. As Ambrose and others [4] have found, initial failure in performing a skill can lead many students to assume that they are inherently weak at that skill. Another problem is that engineering students who have not bridged the gap between general writing and engineering writing are at a disadvantage when writing reports during a summer internship. This film series on writing reports as an engineer is part of a larger collection on communicating as engineers and scientists. All series are available online to any student or faculty member and readily found through web searches of the terms "engineering writing" or "engineering presentations." Because the series on engineering presentations, which has been available for two years, receives substantially more views (28,000 film views in 2020), we anticipate that the series on writing reports will receive more views as engineering faculty learn about it. References 1. L. Reave, "Technical Communication Instruction in Engineering Schools: A Survey of Top-Ranked U.S. and Canadian Programs," Journal of Business and Technical Communication, 18 (4), 452 – 490. 2. "Tutorial on Writing Technical Reports," https://xxxxx.xxx.edu/scientificwriting/tutorial-reports/ (_____________________: _________________________ University, 2020). 3. K. A. Smith, A. F. McKenna, R. C. Chavela Guerra, R. Korte, and C. Swan, "Innovation Corps for Learning (I-Corps™ L): Assessing the Potential for Sustainable Scalability of Educational Innovations," 2016 ASEE Annual Conference & Exposition (New Orleans, Louisiana: ASEE, June 2016), 10.18260/p.25702. 4. S. A. Ambrose, M. W. Bridges, M. DiPietro, M. C. Lovett, and M. K. Norman, How Learning Works: Seven Research-Based Principles for Smart Teaching (San Francisco: Josey-Bass, 2010), pp. 76 – 79.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.289
Teacher spread0.260 · 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