Exploring the Success of GMT Technique: Games, Mind-Mapping, and Twitter Hashtags in Teaching Vocabulary in EFL Higher Education Environment
Bibliographic record
Abstract
Vocabulary is an essential element of language learning. Wide ranges of vocabulary along with grammatical competence guarantee learners to communicate in the language effectively. This study proposes an edutainment method for learning vocabulary by simply combining education and entertainment. This study aims to gain insights about learners’ opinions and perspectives about the use of a technique developed by the researchers as well as how participants feel about their learning. The study investigates the effect of employing Games, Mind-mapping and Twitter Hashtags as the GMT technique, on female Saudi university students’ achievement in English vocabulary. The study suggests that this technique which consists of interactive games, cognitive mind-mapping and the exploitation of technology in the form of twitter hashtags, all employed together, constitute a unified framework for activating students’ vocabulary learning. The sample in the study consisted of 150 students enrolled in the vocabulary building course during the second semester of the academic year 2018/2019. The participants were asked to respond to the questionnaire and they also took variant assessment tests, then their scores were compared to the results of other students who were not taught vocabulary using the technique in question. The findings ascertain the improvement and significant in the experimental group. In addition, the results reveal that the learners had mostly positive opinions on implementing the GMT technique which facilitated their language learning experience. The researchers conclude that the GMT technique can be an effective tool to promote students’ active engagement, motivation, and interaction in vocabulary learning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".