Improving Students’ Vocabulary Mastery by Using Total Physical Response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it