Students' Motivation and Intention Towards Learning Mathematicsand Mathematics Performance: Analysis With their Preferred Trackin the Senior High During the Grade 11 in the K-12 Implementation
Bibliographic record
Abstract
In the light of the ASEAN Economic Integration, the educational system is now geared towards K-12 landscape. This study presents analyses on the students’ motivation and intention towards learning Mathematics, Mathematics performance and students’ preferred track in the senior high during the first batch of the K-12 implementation. A survey was conducted investigate students’ motivation and intention towards learning mathematics, since motivation is perceived as a pre-requisite of and an indispensable component for student engagement in learning. Saeed and Zyngier also cited that, student engagement in learning is not only an end in itself but it is also a means to the end of students achieving sound academic outcomes. A correlation approach was adapted to show relationship among students’ motivation, students’ intention towards learning Mathematics, Mathematics performance, and preferred track in the senior high school. This was conducted at the Basic Education Department - North Campus (BED-NC), University of San Carlos, during 4th quarter of the school year 2014 - 2015. The BED-NC had pioneered the operation of the grade 11 and grade 12 Tech-Voc tracks in Visayas. This serves an experimental batch with thirty eight (38) scholar students sponsored by the University. There were 276 third-year high school students of BED-NC as the respondents of the study. This batch is the first official batch to enrol the grade 11 during the K-12 implementation. The findings revealed the following: a) Mathematics performance has no relationship between the students’ perception on the idea that their friends think that Mathematics is a worthwhile class and students’ intention to continue taking Mathematics classes; b) Mathematics performance has low correlation between the students’ attitude towards doing Mathematics and students’ perception on their ability to do Mathematics; c) The preferred track in the senior high is independent from the students’ perception on the idea that their friends think that Mathematics is a worthwhile class and students’ intention to continue taking Mathematics classes; d) the preferred track in the senior high is dependent from students’ attitude towards doing Mathematics and students’ perception on their ability to do Mathematics; e) The students who preferred STEM have higher Mathematics grade than those who prefer Arts and Design, General Academic, and Humanities and Social Sciences; and f) The students who prefer ABM have higher Mathematics grades than those who prefer Arts and Design. These findings will give direction to those schools who will offer senior high school.
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How this classification was reachedexpand
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".