Kayah Rokayah
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REKONSTRUKSI BEBAN KERJA DAN ALOKASI WAKTU GURU: TRANSFORMASI TATA KELOLA ADMINISTRASI UNTUK EFEKTIVITAS PEMBELAJARAN: (Studi Multi Kasus pada SDN 1 Nagrikidul Kabupaten Purwakarta dan SDN Purnawarman Kabupaten Subang) Ade Tutty Rokhayati Rosa; Nuriskandar; Kayah Rokayah; Surya Hari Mulyana; Sri Rahmawati
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 No. 02 Juni 2026 Press
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.61685

Abstract

Teacher overload in non-pedagogical (administrative) sectors is a primary factor reducing instructional effectiveness in elementary schools. This study aims to reconstruct the workload management and time allocation of teachers through digital-based administrative transformation and structural efficiency. Using a qualitative approach with a multi-case study design, data were collected through in-depth interviews, participant observation, and governance documentation across two demographically, geographically, and culturally contrasting loci: SDN 1 Nagrikidul (Purwakarta District, Purwakarta Regency) managing 24 study groups with 837 students and 35 teachers, and SDN Purnawarman (Compreng District, Subang Regency) managing 6 study groups with 145 students, 10 teachers, and 1 school operator during the 2025/2026 academic year. Data analysis applied the Miles, Huberman, and Saldaña model, encompassing data condensation, data display, and conclusion drawing. The results indicated a chronic imbalance in time allocation, with teachers spending 40%–45% of their weekly working hours on external bureaucratic reporting. The novelty of this research lies in formulating a Comprehensive Time Allocation Reconstruction Model through the taxonomy of 'Teacher Task Differentiation' and single-window digital platform integration. The implementation of this model successfully restored the instructional time allocation up to 80% for deep learning, while transforming school governance from an administrative-mechanical nature into an adaptive-substantif framework at both loci.