The Challenge Of Consistent Grading In Real World, Open Ended Design With Multiple And Multi Disciplinary Instruction
Bibliographic record
Abstract
The S_______ School of Engineering at the University of C____ admits ~730 first year students each fall; these students are required to take a 'Common Core' program for their first year before choosing their field of engineering.As part of the Common Core year, all students take two half courses in Design and Communication.These two courses (ENGG 251 and ENGG 253) are interdisciplinary courses: the teaching team consists of engineers from all disciplines represented at the university (mechanical, electrical, etc); a fine arts instructor, specializing in drawing and sketching; and a technical writing instructor, specializing in written and oral communication.The 251/253 courses are project-based; students work in small groups on real world problems, are required to sketch and document their work and to write formal reports on their projects.While the 251/253 courses present a number of challenges to the instructional team, including the logistics of managing ~730 students and 30 Teaching Assistants, planning five new and unique projects for each academic year and integrating community groups into real-world scenarios, the largest challenge facing the team is that of consistency of assignment design and evaluation.This paper will describe a methodology for maintaining instructional and grading consistency across the many layers of student/tutorial assistant/instructor interaction.Due to the scope of the course, each of the five projects is developed by one or more instructors, with each of the 9 instructors contributing to at least one project.As the instructors come from a variety of backgrounds, consistency has been problematic -what one instructor considers complete assignment information, another considers either woefully inadequate or more detailed than necessary.In addition, as the course is project-based, there are no 'right' answers, only ones that are workable and ones that are not.To further complicate matters, the course emphasizes the design process, so that a group that had excellent process but did not fully succeed at the challenge can still receive an above average grade.Over the last two years, the instructor team has embarked on a new path to ensure consistent and effective assignment design and evaluation.This path, which takes advantage of the team's interdisciplinary strengths, has resulted in a 150% increase in student satisfaction with the course, based on the student rating questionnaires, and has reduces student appeals for remarking by 50%.Page 15.1209.2By standardizing assignment design, allowing for team feedback before assignments are distributed, ensuring consistent updates on project development and providing clearly evaluated exemplars for T.A.s, the instructor team has not only increased student satisfaction, they have ensured a more reliable educational experience for the students, leading to greater student commitment and engagement.
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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 | 0.322 | 0.589 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".