Teaching Materials Development and Meeting the Needs of the Subject: A Sample Application
Bibliographic record
Abstract
It has been determined that the drawings, photographs and pictures related to the subject of the continuity of the tangent function on page 68 of the Ministry of National Education’s twelfth-grade mathematics textbook contradict principles 1, 7 and 10 of Yanpar’s (2007) teaching material development principles. According to these principles, teaching materials should: i) be simple, plain, and understandable, ii) reflect real life as much as possible, and iii) be easy to develop or revise, if necessary. This study aims to develop a portable tangent bridge model to meet the needs of the subject of the continuity of the tangent function. With this aim: i) teaching with the analogies model in the design of the teaching material, ii) “this is my project” format in the development and iii) Yanpar’s (2007) principles were considered. The design of the model lasted 14 weeks. At the end of the study, a portable tangent bridge model from waste products was designed and developed. This model is thought to contribute to the teaching effectiveness of teachers (Shulman, 1987) with content knowledge alongside with pedagogical knowledge (Shulman, 1986). With this contribution, the needs of the subject as described by Taba (1962) and Tyler (1949) will be met. This model will also serve as an example of meeting the needs of the subjects of knowledge and its product, technology, as highlighted by Cahit Arf (Terzioglu &Yilmaz, 2006).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".