The Implementation of Interactive Multimedia Learning Materials in Teaching Listening Skills
Bibliographic record
Abstract
<pre>One of the factors that may affect the success of the learning process is the use of learning media. Therefore, this research aimed to implement and evaluate the interactive multimedia learning materials using Wondershare Quizcreator program and audio materials in teaching 'English listening skills'. The research problem was whether or not there was a significant difference between the results of teaching listening skills through the interactive multimedia learning materials using Wondershare Quizcreator program and the results of teaching listening skills through audio materials. The earlier process used in the research was to produce new products to be implemented. The product had been validated by the experts and tried out to the college students to get their responses towards the validity and the practicality of the products. Furthermore, the products as new materials were implemented to the experimental group and the conventional materials (audio materials) were implemented to the control group. Pretest and posttest had ben conducted before implementation. The results of statistical analysis (SPSS) showed that there was no significant difference between the results of pretest of the two groups, but there was a significant difference between the results of post of experimental and control groups. It was proved that the t-cal. was greater than the t-table (5.583 &gt; 2.000) at df 70 and p.0.05. So, it was concluded that the interactive multimedia learning materials using Wondershare Quizcreator program were effective in teaching 'English listening skills'.</pre>
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".