The Effect of Argumentative Text Pattern Teaching on Success of Constituting Argumentative Text Elements
Bibliographic record
Abstract
The aim of this study is to view how argumentative text pattern teaching influences constituting argumentative essayelements. The study was performed according to one-group pre-test post-test design. The study was carried out inspring term of 2017-2018 academic year and it lasted for five weeks. 33 prospective teachers who took the course ofText Producing Techniques participated in the study. The data of study were collected through 132 argumentativeessays which prospective teachers wrote and Argumentative Text Elements Rubric to evaluate these. The results ofthe research are as follows: Quite few students included argumentative essay elements before argumentative essaypattern teaching and none of the students wrote justification for counter argument. It was determined that 66,7% ofthe students didn’t write data, 81,8% of them didn’t write counter argument, 87,9% did not write justification forcounter argument, 84,8% did not write rebuttal of counter argument and 48,5% did not write conclusion. At the thirdstudy, number of successful students increased on the basis of all elements. However, the majority of students havereached a partially successful level. At the post test, most of the students achieved successful level on the basis of allelements except refuting counter argument. Significant differences on behalf of the post-test were determinedbetween pre-test and post-test scores at all of the argumentative essay elements.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".