The Effects of Activities and Approachments Intended Performance Improvement on the Students’ Performances in Mathematics
Bibliographic record
Abstract
The study presented aims at searching the relation between the learning environment and students’ performances in Mathematics. Data of the 27 week-long research were compiled from 9th grade students at an Anatolian High School. Semi-experimental research modelled pre-test post-test control grouped experimental model was used in the study. Before the application, students’ gaps regarding the subjects were found out and students were supported by it and their initial performances were evaluated by using the developed non-routine problem. During the application, the learning process continued taking the performance variables into consideration in the experiment group whereas the constructivist learning approach was used in the control group in accordance with the mathematics program. Data gained from students’ performance evaluations at the beginning and end of the term, were analysed in order to find out the similarities and differences between the two groups. The findings reveal that there is no meaningful difference in the performance grades of students at the beginning of the term. On the other hand, the average performance grades of the students after the application process show a statistically meaningful difference in favour of the experiment group. Comparisons were made in the experiment group before and after the application.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".