Sustaining Vocabulary Acquisition through Computer Game: A Case Study
Bibliographic record
Abstract
Learning vocabulary is not easy and it may be very frustrating to some learners. Many approaches have been taken to attract learners to learn new vocabulary. The aim of this case study is to explore how a computer game can be adapted as a learning tool to sustain adult vocabulary learning independently. This study used the existing SIM 3 game as a selected platform to investigate the vocabulary learning among ESL adult learners in an independent learning environment. Three adult ESL learners from different backgrounds participated in this study. Learners were examined on how they learned vocabulary and the learning experiences that helped them to gain new knowledge of English vocabulary while playing the game. A combination of mixed-data methods, including playing session observations, semi-structured interviews, a self-report checklist, pre- and post-tests and vocabulary lists, were used to collect extensive data. The findings show that computer games can be beneficial in sustaining language learning, especially in providing space to learn independently. With enough practice and consistent playing, along with the right objective of using it, a computer game can be utilized as a tool for independent learning to learn new vocabulary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".