The Effect of Using Storytelling Strategy on Students’ Performance in Fractions
Bibliographic record
Abstract
Research findings in the field of Mathematics Education emphasize that storytelling is an effective instructional tool in the teaching of mathematics, as it provides a meaningful context that attracts students’ interest and makes learning a pleasant process. The use of stories and fairy tales in the teaching of mathematics motivates students to learn and provides students with an authentic context to understand mathematical concepts and procedures. It is a clear way to incorporate mathematics into other, broader cognitive domains and promotes mathematical discussion in the classroom. The main purpose of this study was to investigate the role that the use of storytelling can play in teaching fractions to third grade students. The study sample consisted of 76 third graders, who attended two primary schools in the city of Florina (Greece). This sample was divided into experimental (n=38) and control (n=38) group. In this study target-focused teaching stories were used. These stories were written in accordance with the objectives of a new Curriculum for rational numbers teaching. The study results showed that the use of storytelling had a positive effect on students’ achievement in fractions, as the experimental group performed significantly better than the control group. The students who benefited most from the use of storytelling were those with medium, especially, with low performance. Finally, the use of storytelling had a positive effect on specific mathematical skills, such as comparing fractions, finding equivalent fractions, creating and manipulating representations and problem solving.
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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 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".