Bibliographic record
Abstract
In 2008 Alberta Education proposed a change of high school mathematics curriculum from the previous Pure Math-10, 20, 30 and Applied Math-10, 20, 30 to Math 10-C, Math 20-1, Math 30-1, Math 20-2, Math 30-2, Math 10-3 Math 20-3 and Math 30-3. The new high school mathematics curriculum was implemented in 2010 and the first group of high school graduates with this new mathematics curriculum got into the post-secondary institutions in 2013.\nWith the old mathematics curriculum, the prerequisite was Pure Math 30 for the students taking mathematics or statistics courses in science and engineering (including nursing) in any post-secondary institutions in Alberta. With the new mathematics curriculum, the prerequisite is Math 30-1 for the students taking mathematics or statistics courses in science and engineering. A question was raised: “Do we allow the students with Math 30-2 to take introductory statistics for nursing degree program?” By comparing the contents of Math 20-1, Math 30-1 and Math 20-2, Math 30-2, the answer was yes from all the post-secondary institutions in Alberta. As a result, the prerequisite for nursing statistics course has been Math 30-1 or Math 30-2 (changed from the previous Pure Math 30) since 2013. What is the impact with this change of the prerequisite? Is there any difference in statistics course performance between the students with prerequisite Math 30-1 and Math 30-2? \nIn the past five years, there were about 1000 students taking the nursing statistics course at Mount Royal University. A sample of 279 students regarding their performance in statistics course and prerequisites was gathered. Statistical analyses indicate significant difference between two groups. Some explanations and suggestions are given in this study.
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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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".