Multimedia environments in the foreign language classroom : effects on the acquisition of the French perfective and imperfective distinction
Bibliographic record
Abstract
This study with young adult learners of French in foreign language classrooms examined the effects of multimedia instruction on the acquisition of a difficult area of second language (L2) grammar in French, namely, the use of the perfective (passe compose) and imperfective ( imparfait) to mark past tense. In particular, the study investigated the impact of two aspects of multimedia instruction on the target grammar development: (a) the type of past tense targeted in instruction, prototypical (i.e., emergent perfective and imperfective forms) and non-prototypical (i.e., advanced perfective and imperfective forms), and (b) learners' readiness for non-prototypical past. The questions were: (a) Is multimedia instruction on non-prototypical past more effective at facilitating past tense improvement than multimedia instruction on prototypical past? (b) Does the L2 learners' past tense readiness mediate the effects of multimedia instruction? To investigate these questions, two multimedia environments with four one-hour lessons were implemented over four weeks in seven university classes in Mexico. Lessons were identical between environments, integrating interactive written/aural narratives and video cartoons to facilitate meaning-oriented past tense production and comprehension. The environments included high past tense frequency, visual enhancement, input processing instruction and feedback to draw learners' attention to past tense. Environments differed only in past tense type: one required prototypical past; the other required non-prototypical past. Using a past tense test, learners (n = 54) were classified as non-ready or ready for non-prototypical past and assigned to one environment. Past tense production and comprehension were assessed using three counterbalanced instruments before, immediately after, and three weeks after instruction. ANOVAs revealed that the environment's past tense type did not have an impact on learners' past tense production growth, whereas learners' past tense readiness did. While non-ready learners exhibited past tense growth immediately after instruction, ready learners did three weeks after instruction. Both groups improved past tense use in accordance with patterns that characterize the acquisition of the perfective and imperfective distinction. No immediate past tense improvement occurred in the comprehension tests, in which the perfective and imperfective differentiation from aural input constituted a challenge. Aspects of the multimedia instruction design and implementation that influenced the treatment results 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".