Design and Empirical Validation of Effectiveness of LANGA, an Online Game-Based Platform for Second Language Learning
Bibliographic record
Abstract
Computer and smartphone-based applications for second language (L2) learning have become popular tools, being integrated in many classroom-based courses and adopted by the public at large. Yet, despite a significant body of research that suggests that individuals differ in their ability to learn L2, it is still unclear what factors predict successful L2 acquisition and how L2 teaching software can be designed to adapt to individuals' strengths and weaknesses. Here, we describe the architecture of LANGA, an online game-based platform under development for L2 teaching and research, and present a demonstrative proof-of-concept study using the platform. LANGA is designed to be both an effective and engaging product from the consumer perspective, and a tool that can be used by researchers to easily implement, deploy and test different training modalities for L2 teaching. Furthermore, key features of LANGA include easy configuration of training via modular design; emphasis on gamified teaching methods; and the use of automated speech recognition to provide learners feedback on verbal production. A first prototype of LANGA was tested in a small-scale, proof-of-concept study. Changes in proficiency from preto post-training were measured using recall and recognition tests, while event-related brain potentials (ERPs) were used to assess changes in brain activity related to lexical access over the course of learning. The results provided initial validation of the platform: participants were able to learn a large proportion of the words taught, and retained the novel words in a two/weeks follow-up. Future directions on the development of the platform are discussed.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".