Improving Students’ Vocabulary Mastery by Using Total Physical Response
Bibliographic record
Abstract
This study aims to describe how Total Physical Response improves students’ vocabulary learning outcomes at the third-grade elementary school Guntur 03 South Jakarta, Indonesia. This research was conducted in the first semester of the academic year 2015 - 2016 with the number of students as many as 40 students. The method used in this research is a Classroom Action Research using the cycle model of Kemmis and Taggart. Class Action Research is conducted through the plan, class action or implementation, observation, and reflection stages. The data collection was done by using a non-test, test instruments and monitoring instruments in the form of action, and field notes. Validity and reliability of the instrument were reached through expert judgment. The results obtained from this study was the improvement in vocabulary learning outcomes of students by applying the Total Physical Response (TPR) method. Percentage of learning outcomes in the first cycle reached 74.13% and 83.38% in the second cycle. The percentage shows improvement of learning effectiveness by applying the Total Physical Response method. The first cycle resulted in an improvement of 64.29% and the second cycle resulted in an increase of 87.14%. Thus, learning process by using Total Physical Response (TPR) can improve students’ vocabulary learning outcomes. The implication of this study is that teaching vocabulary using the Total Physical Response is more effective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".