EXPERIENCES OF NOVICE PUBLIC SENIOR HIGH SCHOOL TEACHERS IN ILOCOS NORTE: A QUALITATIVE DESCRIPTIVE STUDY
Bibliographic record
Abstract
The first year of teaching is a critical phase in the professional journey of educators, often characterized by a steep learning curve. This study explored the experiences of novice public senior high school teachers in Ilocos Norte, focusing on two key questions: (a) What were the experiences of these teachers during their initial year? and (b) What strategies did they employ to manage classroom dynamics and foster student engagement? The study aimed to uncover the experiences and strategies that shaped the first year teaching experience, providing insights to inform supportive educational policies and practices. Using a qualitative descriptive design, data were collected through in-depth interviews Thematic analysis was adopted to analyze the transcripts. The exploration identified recurring themes that shed light on their lived experiences and strategies. Findings revealed a multifaceted narrative encompassing five themes: navigating initial challenges, fostering positive student connections, experiencing supportive school environments, overcoming obstacles, and demonstrating a commitment to reflective practices. Despite facing difficulties such as classroom management and adapting to diverse student needs, teachers employed strategies like game-based learning, ICT integration in education, positive reinforcements, professional growth and continuous learning, and maintaining parent-teacher relationships. The results underscored the importance of a nurturing educational environment in supporting novice teachers’ resilience and professional growth. The study highlighted the complexities of the initial teaching year and offered recommendations to enhance support systems for new teachers. By addressing these insights, stakeholders could create policies and initiatives that fostered teacher resilience and contributed to a thriving educational community.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".