Innovative Second Language Speaking Practice with Interactive Videos in a Rich Internet Application Environment
Bibliographic record
Abstract
Attaining a satisfactory level of oral communication in a second language is a laborious process. In this action research paper we describe a new method applied through the use of interactive videos and the Babelium Project Rich Internet Application (RIA), which allows students to practice speaking skills through a variety of exercises. We present an experience with a number of interactive video exercises used in a real world scenario with nearly 100 students of English as a Second Language at a Spanish university. The use of interactive videos with the Babelium application has allowed our students to record their voices and faces (using microphones and webcams) and to be weekly evaluated according to a set of defined oral evaluation criteria. Based on the results of a survey conducted at the end of the semester, we demonstrate that the students involved in this research experiment have significantly increased the number of hours devoted to speaking practice comparing to the methods used in previous years. Finally, this paper suggests new types of interactive video-based exercises to further improve the outcome of the speaking practice with the help of the Babelium Project application.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".