Investigating the Perceptions of Pre-University Students Concerning the Effectiveness of the Among Us Video Game as a Pedagogical Tool for ESL
Bibliographic record
Abstract
The integration of video games into educational contexts, particularly Among Us, has gained significant attention for its potential to engage and enhance students' learning experiences. In response to the global demand for English lan- guage proficiency, educators are constantly exploring innovative methods to make language learning more engaging and enjoyable. Traditional teaching approaches often struggle to captivate students effectively. This study delves into pre-university students' perspectives on the use of Among Us as a tool for teaching English, aiming to address the re- search gap in this area. Using a quantitative research design, this study collected data from pre-university students through a questionnaire. The findings revealed that students widely believed that Among Us positively contributed to their language proficiency and communicative competence in ESL. The game's interactive nature, requiring commu- nication and collaboration, proved effective in fostering language skills and enhancing motivation. Furthermore, the research explored potential gender differences in students' perceptions of Among Us as an ESL teaching tool. The re- sults indicated that gender did not significantly influence students' views on the game's effectiveness, highlighting its inclusivity. In summary, this study provides valuable insights into the potential of Among Us as a pedagogical tool for ESL. It demonstrates its positive impact on language learning and suggests that it can engage students regardless of their gender. These findings offer educators a compelling avenue to create dynamic and interactive ESL classrooms, ultimately enhancing language proficiency in an engaging and enjoyable manner.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".