Rock or Lock? Gamifying an online course management system for pronunciation instruction
Bibliographic record
Abstract
This one-group quasi-experimental study aimed to determine the effectiveness of using a gamified course management system with points, badges (and consequently competition) to facilitate the development of English phonology in a foreign language context in Japan. To implement this idea, we focused on the acquisition of English segments /r/ and /l/ in production (as in /r/ock and /l/ock respectively). During the study, participants were asked to engage in gamified pronunciation activities over a period of two weeks, using a popular learning site (Moodle). The data collection instruments included pre- and posttests to examine the production development of /r/ and /l/ (using controlled aural elicitation tasks), a written follow-up questionnaire, and user logs to investigate users’ perceptions of the pedagogy utilized. The results indicate that participants benefited from the proposed gamified system for L2 pronunciation instruction, as they improved their production of the target English /r/ and /l/ segments. In addition, responses from the interviews and user logs revealed that participants perceived using the site as enjoyable, anxiety-reducing, and pedagogically useful.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".