The Effect of the Graphic Organizer Strategy on University Students’ English Vocabulary Building
Bibliographic record
Abstract
This study aimed at investigating the effect of the graphic organizer strategy on vocabulary building and vocabulary incremental growth of Jordanian university EFL students. One hundred and two students participated in the study which lasted for one academic semester of four months. Each student enrolled in one of two intact and equally-sized classes of a general English Language course. One of the classes was assigned to an experimental group, whose students were taught eight specific features of vocabulary items using the GO strategy. The eight features were the word’s spelling, pronunciation, part of speech, meaning in the first language, meaning in the foreign language, synonym, antonym and using it in an example sentence. The other class was assigned to a control group, whose students were taught the same vocabulary items using traditional instruction. A pre-test and a post-test were administered to all students whose responses were analyzed using adjusted means, standard errors and an ANCOVA. Results revealed that the experimental group students outperformed those students in the control group concerning their vocabulary building. To decide whether the GO strategy had an incremental growth in students’ vocabulary building, students of both groups sat for three separate evaluative tests. Students’ responses were analyzed using Microsoft Excel sheets and results showed that this strategy significantly improved students’ vocabulary growth over time.
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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 | 0.005 |
| 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 | 0.000 |
| 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".