Bibliographic record
Abstract
As mobile connectedness continues to sweep across the landscape, the value of deploying mobile technology at the service of learning and teaching seems to be both self-evident and unavoidable. To this end, this study employed multimedia to develop three types of vocabulary learning materials. Due to the importance of short-term memory in the realm of vocabulary learning, careful consideration was given to the L2 learners’ different visual and verbal short-term memories. 158 L2 learners aged 18-23 participated in the major phases of vocabulary learning experiment through mobile. Based on their scores on the English Vocabulary and Recall tests and statistical analysis of the results it was revealed that L2 learners with high-visual and high-verbal abilities find it easier to learn the content presented with both pictorial and written annotations. However, L2 learners with low-visual and low-verbal abilities benefit from learning materials presented without annotations. Furthermore, delivery of learning materials with pictorial annotation to learners with high-visual ability and the delivery of learning materials with written annotation to learners with high-verbal ability result in better vocabulary learning. The findings of this study could perform as a roadmap in creating learning materials for mobile learning English language.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".