Evaluating the use of web-based games on students' vocabulary retention
Bibliographic record
Abstract
Today, the use of technology has made education more enjoyable. Vocabulary retention becomes a challenging task for both teachers and learners. They may learn the vocabulary but may not retain it. Yet, the use of web-based games may assist them in maintaining words known for short and long-term retention. The current study was conducted to identify students' abilities in retaining words learned after they were assigned to play a web-based vocabulary learning game, namely OnVac. Both short and long-term retention were measured after they were required to play the game as a treatment. Also, the study investigated students' perceptions about the system operation of OnVac. The use of quantitative research design, particularly quasi-experimental research and survey showed gains in vocabulary retention among students for short and long-term retention. The study also found that OnVac can support vocabulary learning among students in their learning of the specialized vocabulary. In terms of the system operation, the participants reported that the tool could assist them in learning engineering and technology words as it was convenient to use. The study provided implications to the teaching pedagogy in that teachers need to be wise and analytical in developing the online game to assist learners in learning English specialized vocabulary.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".