Investigating the Effect of Using Multiple Sensory Modes of Glossing Vocabulary Items in a Reading Text with Multimedia Annotations
Bibliographic record
Abstract
In recent years, improvements in technology have enhanced the possibilities of teaching and learning various subjects. This is specially the case in foreign language instruction. The use of technology and multimedia brings new opportunities for learning different areas of language. In this regard, the present study attempts to find out if the use of multimedia, images and movies, helps learners in learning vocabulary items included in a reading comprehension text. For this purpose, 70 students studying English at pre-intermediate level have been selected. These participants are then divided into three groups, each of which receives a different kind of instruction. The members of the first group were required to read some texts in which certain vocabulary items were included as the target of teaching. Those in the second group received the same texts with some pictures added so that the grasp of the unknown words would become facilitated. The students in the third group were exposed to the same material along with some movie strips. The strips were selected in a way to include the specific vocabulary items. The course duration was about 45 days. At the end of the course, all the students in the three aforementioned groups were sat for taking a vocabulary test. The test format was multiple-choice. The results of the ANOVA indicated that annotating reading comprehension passages with movie clips contributes to better learning and recall of vocabulary through reading texts.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".