WIP: Short Online Films to Help First-Year Students Write Reports as Engineers
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".