A Correlational Study on the Teaching Methodologies and the Competencies of Graduates in a Private University in the Philippines
Bibliographic record
Abstract
Teaching and learning methodologies are significant factors in building students’ competency after college life, making them a good contender in the labor market. The study determined the impact of the varied methodologies in teaching and learning on the graduates' competency. The 181 graduate respondents participated in the survey on a snowball method in data gathering. Frequency and simple percentage, weighted mean, Chi-Square Test of Independence, and One-way ANOVA were used to treat and interpret the data. The findings revealed that, in a pervasive way, the teaching and learning methodologies among faculties embodied in the flipped classroom, project-based learning, cooperative learning, problem-based learning, and competency-based learning in the Department were perceived by the graduates. By this instance, further findings revealed a significant relationship with adopting these varied methodologies and its influence on the graduates' competency in oral/written communications, teamwork/ collaboration, information/ technology application, leadership, and professionalism/ work ethic. The study concluded that a more substantial imposition of teaching and learning methodologies to the students' could greatly emphasize graduates' professionalism, leadership, communication, collaboration, and knowledge in information technology. Furthermore, the influence of flipped classrooms, project-based learning, cooperative learning, problem-based learning, and competency-based learning in the teaching and learning experiences in the Department provides a good impact on their competency as a graduate. These practices indicate a strong alignment between the institution's interests which focuses on producing competent and innovative graduates that are efficient and effective in the labor market.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".