Incorporating Learning Motivation and Self-Concept in Mathematical Communicative Ability
Why this work is in the frame
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Bibliographic record
Abstract
<p class="apa">This research is trying to determine of the mathematical concepts, instead by integrating the learning motivation (X<sub>1</sub>) and self-concept (X<sub>2</sub>) can contribute to the mathematical communicative ability (Y).</p><p class="apa">The test instruments showed the following results: (1) simple regressive equation Y on X<sub>1</sub> was <em>Ŷ</em> = 32.891 + 0.43X<sub>1</sub>, simple linier regressive test Y on X<sub>1</sub> was F<sub>cal</sub> = 1.272&lt; F<sub>tab</sub> = 1.897 and pertained to linear regression at significant level of 5%, (2) simple regressive equation Y on X<sub>2</sub> was <em>Ŷ</em> = 33.68 + 0.44X<sub>2</sub>, simple linear regressive test Y on X<sub>2</sub> was F<sub>cal</sub> = 0.616&lt; F<sub>tab</sub> = 1.897 and pertained to linear regression at significant level of 5%.</p><p class="apa">The data analysis of the variable correlation could be seen as follows: (1) learning motivation (X<sub>1</sub>) with mathematical communicative ability (Y) was r<sub>cal</sub> = 7.730&gt; r<sub>tab</sub> = 4.020 indicated the positive correlation at significant level of 5%, (2) self-concept (X<sub>2</sub>) with mathematical communicative ability (Y) was r<sub>cal</sub> = 8.375&gt; r<sub>tab</sub> = 4.020 showed the positive correlation at significant level of 5%.</p><p class="apa">The result of this study is that there was a positive relationship between learning motivation (X<sub>1</sub>) and mathematical communicative ability (Y), and also self-concept (X<sub>2</sub>) and mathematical communicative ability (Y).</p>
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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