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Record W3033612689 · doi:10.1558/cj.36996

Rock or Lock? Gamifying an online course management system for pronunciation instruction

2020· article· en· W3033612689 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCALICO Journal · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsConcordia University
Fundersnot available
KeywordsPronunciationContext (archaeology)PsychologyPerceptionComputer scienceMathematics educationLinguistics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.292
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it