DESCRIPTORS *Assignments; *Computer Uses in Education;
Bibliographic record
Abstract
Teachers must strive to enhance their power as educational connoisseurs and critics, professionals who understand how to use technological learning materials that promote growth across multiple intelligences. This case study demonstrates how this demand was met in an "Advanced " Grade 10 English class at Hillcrest High School in Ottawa, Canada. The project came to be known as the MILT.CEU assignment: "MILT " stands for Harvard Professor Howard Gardner's multiple intelligences learning theory and CEU was taken from the title of the computLr-based learning materials Cloze Encounters Unlimited. Cloze Encounters is, in its simplest form, DOS (Disk Operating System) software that allows students to work individually or in teams to create puzzles for other students to do. The assignment consisted of designing a puzzle that could be carried out in each of seven traditional subject domains: language arts,
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.494 | 0.284 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".