Evaluating Telegram Application to Empower the Students’ Vocabulary Mastery
Bibliographic record
Abstract
The biggest trigger for the students’ in mastering vocabulary is learning media. The inexistence of good learning media will affect the students’ vocabulary mastery. One of the learning media that is much promoted and used during the pandemic of Covid-19 is the telegram application. Therefore, this research aims to measure the use of telegram applications as learning media to enhance the students’ vocabulary mastery. In this research, the researcher applied a quasi-experimental method. The population of this research was the seventh-grade students at UPTD SMP Negeri 22 Barru. The samples of the research were taken using the cluster random sampling technique, there are two classes as samples, experimental class, and control class, both classes consisted of 28 students. The data was collected using vocabulary tests (pre-test and post-test) and analyzed employing statistical calculations to test the hypothesis. The result of this research shows that the mean score for pre-tests was 45.35 and the post-test was 83.57. Besides the different scores for pre-test and post-test, the mean score of the students in post-test was 83.57 is higher than the Kriteria Ketuntasan Minimal (75) in UPTD SMP Negeri 22 Barru. The result of the t-test value in the post-test was 2.214 and the t-table value was 1.684. It means that H1 was accepted and H0 was rejected and the seventh-grade students at UPTD SMP Negeri 22 Barru who are taught by using the telegram application have better vocabulary mastery than the seventh-grade students who are taught without using the telegram application.
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".