Teaching Vocabulary Through Wiki to First Secondary Graders
Bibliographic record
Abstract
Vocabulary is a fundamental component of any language. Learning vocabulary plays a crucial role in learning English language. Whereas wiki is a promising Web 2.0 technology that can be used innovatively in EFL instruction. This study aimed at examining the effectiveness of wiki-based instruction on vocabulary learning of first secondary graders. It was carried out in Sabya, Jazan, Saudi Arabia in 2018. It followed the quasi-experimental design of one experimental group. Fifty-seven Saudi teenage EFL students participated in a researcher-designed wiki-based vocabulary course in a random male school. The course consisted of twelve lessons to teach 80 words picked from the grade’s English Schoolbook (Mega Goal 2) to ensure their importance and benefit to students. Vocabulary pre-test and post-test along with a closed observation card were used to collect data. Descriptive statistics, paired samples and one-sample t-tests were used for analysis. It was found that wiki had slight positive effect on vocabulary learning. Results demonstrated that students achieved significantly better marks in their post-test but with a very low effect size (0.32). In addition, wiki was observed as usable, motivating, vocabulary enlarging assistant, and can be perceived positively by students. In the contrary, it was observed that the students’ collaborative work level was low. Accordingly, wikis can be a good vocabulary teaching tool if well-designed and well-implemented after training both teachers and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".