Academic Governance, Self-Regulation, and Productivity of School Heads: Bases for Learning and Development Framework

This descriptive-correlational study assessed the levels of academic governance, self-regulation, and productivity among school heads considering their sex, age, civil status, educational attainment, and administrative experience. It also examined the extent to which self-regulation and academic governance influence productivity. The aim was to enhance school heads’ competencies in these key areas. Data for this study were gathered using researcher-developed, expert-validated questionnaires. Descriptive statistics (frequency, percentage, mean, and standard deviation) and inferential test (Linear Regression at a 0.05 significance level) were conducted using SPSS. Findings revealed that school heads exhibited very good levels of academic governance and very high self-regulation. Productivity was also consistently high across 14 indicators. Notably, both self-regulation and academic governance significantly influenced productivity, with self-regulation exerting a stronger effect. Based on the results, a learning and development framework was formulated, encompassing three proposed initiatives: Project CRFL, Project Lead Within, and Project eLead. These initiatives aim to strengthen leadership by fostering emotional intelligence, enhancing collaboration, and cultivating future-focused skills. The study concludes that effective leadership stems more from internal capacities than demographics, highlighting the need for continuous, context-specific training for school heads.

Institutions

Publication Details

Published
2025-07-19
DOI
https://doi.org/10.69478/best2025v1n1a022
Primary Topic
Migration, Health and Trauma
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Academic Governance, Self-Regulation, and Productivity of School Heads: Bases for Learning and Development Framework

Migration, Health and Trauma
article

Academic Governance, Self-Regulation, and Productivity of School Heads: Bases for Learning and Development Framework

article en

Abstract

This descriptive-correlational study assessed the levels of academic governance, self-regulation, and productivity among school heads considering their sex, age, civil status, educational attainment, and administrative experience. It also examined the extent to which self-regulation and academic governance influence productivity. The aim was to enhance school heads’ competencies in these key areas. Data for this study were gathered using researcher-developed, expert-validated questionnaires. Descriptive statistics (frequency, percentage, mean, and standard deviation) and inferential test (Linear Regression at a 0.05 significance level) were conducted using SPSS. Findings revealed that school heads exhibited very good levels of academic governance and very high self-regulation. Productivity was also consistently high across 14 indicators. Notably, both self-regulation and academic governance significantly influenced productivity, with self-regulation exerting a stronger effect. Based on the results, a learning and development framework was formulated, encompassing three proposed initiatives: Project CRFL, Project Lead Within, and Project eLead. These initiatives aim to strengthen leadership by fostering emotional intelligence, enhancing collaboration, and cultivating future-focused skills. The study concludes that effective leadership stems more from internal capacities than demographics, highlighting the need for continuous, context-specific training for school heads.

Vol. 1(1)
University of Antique (PH)
Decent work and economic growth
Openalex Percentile: Top 38%
Migration, Health and Trauma
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.