A Curriculum Collaboration Model: Working With Upper Division Students To Improve A First Year Program
Bibliographic record
Abstract
This paper presents an overview of a quarter-long design-build project in the Fundamentals of Engineering (FE) course sequence, which is part of the First-Year Engineering Program at The Ohio State University (OSU).The current design-build project is discussed along with a justification for the need to institute a replacement.The primary focus of this paper is a unique collaboration model which was developed to address this need.Faculty, staff, and graduate teaching associates from the First-Year Engineering Program joined with the Industrial, Welding and Systems Engineering (IWSE) Department to investigate possible solutions.The paper describes the curriculum research and design methods used by the curriculum team.The document also discusses the requirements and constraints of the project and presents a detailed timeline of the evaluation and feedback tools implemented.The evaluation and feedback tools used are explained along with sample worksheets.The results of the first quarter are discussed in light of the constraints and requirements of the FE program.Finally, the improvements from the second quarter trials are further explained.This paper will provide clear examples of the project's various cycles, discussion of the planned implementation process, and examples of the final roller coaster design.The collaboration model is reviewed, with experiences gained and future plans presented.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".