Bibliographic record
Abstract
An approach to course planning in educational programs was presented in Madan and Gnanendran (2020). There, each student's journey through a degree program was viewed as a "project" requiring completion of a set of tasks (courses) with each task having a specific duration (semester) and, possibly, precedence requirements (prerequisites). The institution is expected to offer courses in an appropriate sequence and timing so that students may progress toward graduation efficiently. Given the large number of students that could matriculate every semester, the institution needs to manage myriad individual degree pathways. To make this problem tractable, Madan and Gnanendran (2020) considered cohorts of students, rather than individuals, according to when each entered the program. The institution then only needs to manage a limited number of simultaneous projects with outcomes that are measured on the typical criteria of time and cost. Recently, researchers (e.g., Rigby et. al., 2016) have espoused the "agile" approach over the traditional "waterfall" approach to managing projects in environments where changes to requirements are to be expected, the work can be modularized, and there are avenues to collaborate with end-users. Since degree programs possess all of these characteristics, we propose heuristics based on agile methodology to address course planning and demonstrate their application via numerical examples.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".