Teacher Readiness in STEM–Deep Learning: A Cluster-Based Approach to Tiered Professional DevelopmentTeacher Readiness in STEM–Deep Learning: A Cluster-Based Approach to Tiered Professional Development
Keywords:
Teacher readiness, STEM, Deep Learning, cluster analysis, professional developmentAbstract
Penelitian ini mengkaji kesiapan guru dalam mengimplementasikan pembelajaran STEM (Science, Technology, Engineering, and Mathematics) yang berorientasi pada Deep Learning, dengan tujuan merancang kerangka pengembangan profesional bertahap. Menggunakan pendekatan kuantitatif dengan desain survei potong lintang, data dikumpulkan dari 115 guru pada berbagai jenjang pendidikan di Provinsi Bali. Kesiapan guru diukur melalui lima dimensi utama: penguasaan dasar STEM, literasi Deep Learning, self-efficacy teknologi, keterampilan penggunaan aplikasi, dan kesulitan pembelajaran yang dirasakan. Analisis data menggunakan K-Means Cluster Analysis mengidentifikasi tiga profil kesiapan yang berbeda: Kesiapan Tinggi (skor tinggi di semua dimensi, hambatan minimal), Kesiapan Sedang (kuat pada STEM dan integrasi teknologi, namun hambatan moderat dan literasi DL lebih rendah), dan Kesiapan Rendah (skor rendah di hampir semua dimensi, hambatan tinggi). Setiap klaster menunjukkan kebutuhan pengembangan profesional yang berbeda, mulai dari peran mentor dan inovator bagi guru dengan kesiapan tinggi, hingga penguatan dasar dan pendampingan intensif bagi guru dengan kesiapan rendah. Temuan ini menegaskan pentingnya model pelatihan terdiferensiasi berbasis data yang mengintegrasikan kerangka Mindful, Joyful, Meaningful untuk meningkatkan kompetensi teknis sekaligus kedalaman pedagogis. Pendekatan ini memiliki implikasi bagi kebijakan pendidikan, praktik pelatihan guru, dan penelitian lanjutan yang mengaitkan profil kesiapan guru dengan hasil belajar siswa.
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