"The Influence of the K-12 Curriculum on College Education and Career Pathways of Peñaranda National High School's 2018 Science Technology Engineering and Mathematics (STEM) Graduates"
Bibliographic record
Abstract
This is an exploratory investigation of the STEM graduates’ perceptions of their competence and their teachers in the different senior high school subjects including their college career decisions and success stories. Using quantitative and qualitative research methods, 73 STEM graduates of Peñaranda National High School in the academic year 2017-2018 who were already college graduates in SY 2022-2023 were followed up and profiled. Results revealed that they finished Bachelor of Science programs in Civil Engineering (27.4%), Nursing (16.44%), Electrical Engineering (13.69%), Agriculture (12.33%), and Mechanical Engineering (10.96%). Their GWA for general education is 89.58 major subjects are 84.56 and professional subjects are 88.79. 30.14% of them already have eligibilities, 2.7% were regular/permanent, 38.4% were contractual and most of them were preparing for their board/licensure exams. The students assessed the effectiveness of their teachers in delivering instruction on core, applied, and specialized subjects in Senior High School (SHS). The mean scores exhibited a range between 3.05 and 3.30, thereby classifying them inside the "Proficient in Teaching" group. Interestingly, suggestions for instructors to enhance their performance in each of these domains also aligned with the "Proficient in Teaching" classification. This finding demonstrates a correlation between the effectiveness of teachers in their instructional practices and the degree to which student feedback contributes to their pedagogical improvement. Upon examining academic achievement, it was discovered that a significant proportion of students attained grades categorized as "Outstanding" or "Very Satisfactory" over their high school and college years. Upon closer examination, it became evident that there existed a direct correlation between the academic performance of students and the effectiveness of their teachers in delivering instruction. The study substantiates the significance of effective instruction in fostering the academic achievement of students. It is posited that an average value of 4 is considered optimal, denoting a level of "Highly Proficient" for teacher performance and "Outstanding" for student academic performance. The findings of this study provide valuable insights into strategies for enhancing the quality of instruction and educational outcomes in secondary STEM curricula. The aforementioned findings demonstrate the significance of teachers possessing a comprehensive understanding of fundamental, practical, and specialized subjects concerning the scholastic achievements of the students.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".