Effect of Repeated Testing to the Development of Vocabulary, Nominal Structures and Verbal Morphology
Bibliographic record
Abstract
The repeating testing has shown to increase the general proficiency level of the students. Metsämuuronen (2013)showed with an experimental study that the overall achievement level in a secondary language enhancedstatistically significantly whit repeated testing design. Previously, Tuvling (1967) and Karpicke & Roediger(2008) showed with a laboratory experiment that remembering the material studied is the most efficient withrepeated testing sessions rather than with repeated studying sessions. An explanation for this, given by Lasry,Levy and Tremblay (2008), is that the repeated testing leads to multiple traces to the memory, which optimizesrecall. This study concentrates on the increase in the proficiency level in Vocabulary, Nominal structures andVerbal Morphology after the set of exhaustive testing sessions. It also reviles change in the proficiency levels ofthe students in these areas during the study process. The experimental group gained more than the control groupin all areas though the difference is statistically significant only in the content of Vocabulary. The effect sizes arehigh (Cohen’s d > 1.0). In all areas of interest, the learning curve was of wide U-shape after the elementaryperiod of studies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".