Is Math Adequately Taught for Tomorrow’s Software Engineers?
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
The 2012, July 28 New York Times, Sunday Review, The Opi nion Pages editorial “Is Algebra Necessary?” [1] raised once more, not only in the U.S., legitimate questions on math teaching in high school and college. Published com ments, both on the newspaper’s Reader’s Comments sec tion, as well as elsewhere in the Internet (e.g. the LinkedIn ACM Group discussion on [1] are somewhat surprisingly as suming that [1] implies suppressing school algebra stu dies (although only its title might mislead–but titles are of en used to better sell the paper–,as no such thing is stated in [1]), and are clearly divided into two categories: those that still fear math, and especially algebra (and would glad ly applaud suppressing it immediately), and those that ei ther love, like, or/and use it directly (and are outraged by such a possible suppression). Being involved for some 35 years both in the IT industry (mostly in Sofware En gi ne er ing) and in University Computer Science teaching, but also be ing a father (of both IT and nonIT graduates, as well as of very young pupils), I consider this topic a very im por tant and challenging one, even when restricted to math tea ch ing for tomorrow’s IT and, especially, Sofware En gi ne ers. [1] starts from U.S. wide statistics showing that mainly al ge bra, but math in general too, is the main obstacle that blocks more than 40% of the students both in high school gra duation and college enrollment. This is true also in other countries, including mine: let’s call them for the rest of this pa per the unfortunate ones. As, fortunately, there are also coun tries where this is not happening (e.g. Germany, Japan, Fin land, South Korea, Canada, etc. let’s call them here the for tunate ones), it is clear that there exist solutions for sig ni ficantly improving at least the above percentage. This pa per tries to summarize some basic facts and widespread re le vant opi nions in this area, and concludes with some sug ges tions aim ing at better teaching math, both generally and, es pe cial ly, for tomorrow’s IT engineers, with emphasis on sof ware ones.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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