Incorporating Learning Motivation and Self-Concept in Mathematical Communicative Ability
Bibliographic record
Abstract
This research is trying to determine of the mathematical concepts, instead by integrating the learning motivation (X1) and self-concept (X2) can contribute to the mathematical communicative ability (Y). The test instruments showed the following results: (1) simple regressive equation Y on X1 was Ŷ = 32.891 + 0.43X1, simple linier regressive test Y on X1 was Fcal = 1.272< Ftab = 1.897 and pertained to linear regression at significant level of 5%, (2) simple regressive equation Y on X2 was Ŷ = 33.68 + 0.44X2, simple linear regressive test Y on X2 was Fcal = 0.616< Ftab = 1.897 and pertained to linear regression at significant level of 5%. The data analysis of the variable correlation could be seen as follows: (1) learning motivation (X1) with mathematical communicative ability (Y) was rcal = 7.730> rtab = 4.020 indicated the positive correlation at significant level of 5%, (2) self-concept (X2) with mathematical communicative ability (Y) was rcal = 8.375> rtab = 4.020 showed the positive correlation at significant level of 5%. The result of this study is that there was a positive relationship between learning motivation (X1) and mathematical communicative ability (Y), and also self-concept (X2) and mathematical communicative ability (Y).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".