Identifying Common Errors of the First Graders in the Writing of Vertical Numbers
Bibliographic record
Abstract
The aim of the study is to investigate the errors that first grade students have made in their writing of verticalnumbers which have just been applied by removing cursive writing. Considering the aim of the study, verticalnumber writing styles of the first-grade students in primary school were analyzed. The sample of the study consistsof 116 students who are studying in the first grade of primary school. The study was defined as a case study. A datacollection tool was developed for determining the mistakes that students made while writing the vertical numbers inline with the aim of the research. Through the data collection tool, all numbers from 0 to 9 are given as writtenstatements and it is required to write the numbers in the spaces left under them. The results obtained from theanalysis of the data include the existing types of errors that are relevant to the number writing in the students after thefirst literacy teaching processes. According to the results of the research, writing the numbers oblique,vertical-horizontal-diagonal straight lines are drawn in a curvilinear style, curvilinear and circular lines are distorted,numbers are not aligned in the direction of writing, and some numbers are written in reverse have been seen as themost common errors. In accordance with the types of errors identified in the research, it is thought that the emphasison dictation studies to increase the awareness of students will decrease these types of errors and their frequency. It isalso stated that it is important to diversify the related studies as much as possible, taking into consideration theindividual differences of the students.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".