Implementation TANDUR Learning Using GeoGebra Towards Student Learning Result Viewed from Independence Learning
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
The purpose of this study was to determine the effect of the TANDUR learning model assisted by GeoGebra on mathematics learning outcomes in terms of student learning independence which is a quasi-experimental research. The number of respondents in this study were 71 students. Collecting data on learning independence is collected through a questionnaire, and data on mathematics learning outcomes are collected through tests of mathematics learning outcomes in the cognitive domain. Data analysis was performed using two-way analysis of variance (ANAVA) with the help of SPSS, at a significance level of 5%. The results show that the learning outcomes of students who use the TANDUR learning model assisted by GeoGebra are better than the learning outcomes of students who use conventional learning models, student learning outcomes using the TANDUR learning model assisted by GeoGebra are better than using conventional learning models in students who have learning independence high, and the learning outcomes of students who use conventional learning models are better than students who use the GeoGebra-assisted TANDUR learning model for students who have low learning independence.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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