The Effects of Argumentation-Based Teaching on Primary School Students’ Academic Achievement, Science Attitudes and Argumentative Tendencies
Bibliographic record
Abstract
This research aims to analyze the effects of argumentation-based teaching (ABT) on the 4th-grade students’ academic achievement, argumentative tendencies and attitude towards science. The universe of the research was 4th-grade students studying in Yığılca district of Düzce province in the 2017-2018 academic year, in Turkey. The sample of the research consists of 37 4th grade students studying in two different classes. The pretest-posttest matched control group design was used which is one of semi-experimental design techniques. While activities related to ABT were administered to the experimental group, the existing curriculum was applied for the control group. The data of the research were collected using three tools: science achievement test (AT), science attitude scale (SAS), and argumentativeness scale (AS). All data collection tools were administered to experiment and control groups as pre-test and post-test to determine if there was a difference after the application. The findings revealed that the academic achievement of the students was significantly influenced by the activities related to ABT. However, there was no significant difference between experimental and control groups in terms of argumentative tendencies and their attitude towards science.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".