Making a little theory go a long way: situating rubrics for learning and assessing
Bibliographic record
Abstract
Connecting theory and practice requires teachers of technical communication to find a balance between deepening students' appreciation of theoretical principles and giving students the practical advice they want and, in fact, need. Inherent in this challenge is another: teaching communication efficiently and economically. At the University of Toronto Faculty of Applied Science and Engineering, we are exploring a promising approach. In a new first year design course, currently in a pilot phase with 150 students but scheduled to expand to 900 in the fall of 2005, we are implementing grading rubrics designed both as assessment tools and as guides to enable students to instruct themselves as they move from assignment to assignment. Using rubrics with an eye to their situated mediational aspects allows a sharing of learning responsibilities. The data from our current use of rubrics adds to prior and recent research in engineering populations in the US and Canada demonstrating how rubrics act to instantiate a two-way dynamic by: 1. providing response to student work that both articulates clearly the requirements for acceptable/excelling work, 2. making key information for assessment available both before and after the work of an assignment so that the student can directly experience the application of theory in planning and reviewing documentation activities instead of only being able to match up standards articulated after production. 3. allowing student and teacher to establish a point of view that both focuses on current demands and refers to expectations of a community of practice.
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.
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 | 0.081 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".