Teaching Case: The Course Management Process for an Undergraduate IT Class at Scholastic University
Bibliographic record
Abstract
This case is available to instructors at no charge for teaching purposes. Please contact the author to receive a copy of the accompanying teaching notes. The case teaching notes will only be distributed to instructors with proof of status (webpage) and will be sent at their institution e-mail. Please DO NOT distribute the teaching notes to students. Teaching Case: Factual case that masks the identity of the company/individuals. This case was evaluated by the editorial committee. Scholastic University is a small Canadian university. Until fifteen years ago, Scholastic was primarily a teaching institution. Over the last few years, greater emphasis was placed on Scholastic's research mandate. New hires are expected to produce high-quality research and to demonstrate teaching excellence. Professor Charlene Smith is a new tenure-track assistant professor of information systems. Each term, she teaches three sections of BMOD101, Business Modeling. As well as teaching, Professor Smith must work on her publications to build her tenure and promotion file. Because BMOD101 represents a heavy workload, Smith realizes that she may not be able to meet her research commitments. Your mandate is to analyze the course management process for BMOD101 and provide Smith with a solution that leverages information technologies to optimize her time and that of teaching assistants as well as other course resources.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".