ASSESSMENT DESIGN THROUGH AN EXPERT SYSTEM AND ITS APPLICATION TO A COURSE OF HYDRAULICS
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
An assessment consists of questions addressing the required learning outcomes of a course. If a pool of questions of various types is made available then assessment design reduces to selection of questions, one by one, from the pool. Since the number of possible questions for a course may be quite large, and several preferences have to be matched, manual selection of a suitable question is not possible. This paper presents an enhanced implementation of a previously presented idea of a methodology for assessment design with an application to a course of Hydraulics with an initial pool of 1,000 questions. Each question is tagged with a set of attributes. The rules are generated by the expert system itself. The idea of a score of relevance has been introduced. The enhanced implementation displays a set of questions with their relevance scores rather than a single question to let the instructor choose from them. An instance of MS SQL Server at Azure database is used for the web-based cloud implementation.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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