USING POST COURSE ASSESSMENTS TO INVOLVE INSTRUCTORS IN THE CONTINUOUS IMPROVEMENT PROCESS
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.
Bibliographic record
Abstract
Abstract – Continuous improvement is a critical aspect of engineering program development and accreditation. Instructors are important stakeholders who can provide valuable feedback with regards to courses and curriculum; however, obtaining this information can be problematic. Here we present a post course assessment system (PCAS) that enables all instructors to provide timely and specific feedback about their courses as well as pause to reflect on the pedagogical successes and challenges they have faced over the course of a semester. The PCAS also serves a number of program specific uses (triggers, graduate attributes, consistency). The system has been very successful if providing course-based information and, taken in aggregate, program-based insight. The system continues to be adapted but is a good model of instructor engagement and feedback mechanism.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it