Using the French Tutor Multimedia Package or a Textbook to Teach Two French Past Tense Verbs
Bibliographic record
Abstract
This paper examines the difference in learning outcomes between two groups of students, one of which used the French Tutor, a multimedia package, and the other a textbook to learn the formation and use of two French past tense verbs: the perfect and the imperfect. Unlike the textbook, the French Tutor included visual effects, intelligent feedback, drag-and-drop exercises, a variety of exercises of graduated difficulty, and the game “Who wants to be a millionaire?” Both groups of students were administered a pre- and posttest on the formation and use of these two verb tenses. The French Tutor group performed significantly better than the textbook group. A questionnaire asking for comments on the effectiveness of the French Tutor software was also given to the French Tutor group. All students acknowledged that the French Tutor software helped them acquire a better understanding of these two tenses and reported that the features that contributed most to their understanding were the exercises and the “Who wants to be a millionaire?” game. Discussion of the results follows, and suggestions are made for further research.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".