Teachers' Views on the Problem-Solving & Problem-Posing Tasks in Primary School Mathematics Textbooks
Bibliographic record
Abstract
This paper aims to reveal teachers' views on the problem-solving & problem-posing tasks of primary school mathematics textbooks. The study utilized the single-case embedded design. We examined the mathematics textbooks used in the Turkish public schools in the 2017-2018 academic year. Also, we interviewed twelve class teachers, who were determined by the criterion sampling method, to reveal their views on the textbooks. In order to collect data, we employed a "Data Coding Scheme" to determine the problem-solving & problem-posing tasks in the textbooks and a "Semi-Structured Interview Form" composed of two open-ended questions to find out about the teachers' views. Since the questions asked to the teachers were determined as themes in advance, the descriptive data analysis technique was used for data analysis. The study concludes that the examined mathematics textbooks contain a sufficient number of problem-solving tasks, which are equally distributed under each heading in the textbooks. On the other hand, it has been found that the textbooks contain a limited number of problem-posing tasks, which are not equally distributed under each heading. Furthermore, we determined that no textbook that we examined contains different types of problem-posing tasks. Also, according to the teachers whom we interviewed, although the number of problem-solving tasks in the textbooks is sufficient, the tasks should be more effective. They also stated that the textbooks contain very few problem-solving tasks requiring different problem-posing strategies.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".