Mohamad Azhar Annawa UBM
Institut Agama Islam Ngawi

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Pengaruh Model Problem-Based Learning Berpendekatan Deep Learning terhadap Higher Order Thinking Skills dan Motivasi Belajar Siswa Mohamad Azhar Annawa UBM; Anik Faridah; Naufal Diama Putra; Khubaib Al Fachri; Isma Silvi
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 04 (2025): Volume 10 No. 04 Desember 2025 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i04.36555

Abstract

Penelitian ini bertujuan untuk menguji pengaruh model Problem-Based Learning (PBL) berpendekatan deep learning terhadap Higher Order Thinking Skills (HOTS) dan motivasi belajar siswa kelas IV pada mata pelajaran Pendidikan Pancasila di MI Al-Falah Beran Ngawi tahun pelajaran 2025/2026. Penelitian menggunakan desain quasi experimental nonequivalent control group dengan sampel 21 siswa kelas eksperimen dan 18 siswa kelas kontrol. Instrumen penelitian meliputi tes HOTS berbasis indikator C4 hingga C6 dan angket motivasi model ARCS. Analisis data mencakup statistik deskriptif, uji asumsi, dan ANCOVA dengan pretest sebagai kovariat. Hasil penelitian menunjukkan bahwa PBL berpendekatan deep learning berpengaruh signifikan terhadap HOTS (F = 9,09; p = 0,005; η²p = 0,197) dan motivasi belajar siswa (F = 49,4; p = 0,000; η²p = 0,572), keduanya berada pada kategori pengaruh besar. Dengan demikian, Hₐ diterima dan H₀ ditolak. Temuan ini menunjukkan bahwa integrasi antara PBL dan deep learning dapat meningkatkan kemampuan berpikir tingkat tinggi dan motivasi belajar secara signifikan. Penelitian ini memberikan kontribusi empiris dalam pengembangan strategi pembelajaran inovatif di tingkat dasar untuk mendukung pembelajaran abad ke-21.
Tren, Dampak, dan Tantangan Implementasi Artificial Intelligence dalam Pendidikan Dasar di Indonesia: A Systematic Literature Review Mohamad Azhar Annawa UBM; Wulan Feronika Maharani; Lathif Abdul Razaq
Bima Journal of Elementary Education Vol. 4 No. 1 (2026): Bima Journal of Elementary Education
Publisher : STKIP Taman Siswa Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37630/bijee.v4i1.4262

Abstract

Low literacy and numeracy achievement, along with disparities in the quality of learning in Indonesia, necessitate adaptive technological innovations, one of which is the use of artificial intelligence (AI) in primary education. This study aims to map the forms and trends of AI implementation, analyze its impact on learning, and identify challenges and supporting factors at the primary education level (SD/MI). This study employed a Systematic Literature Review (SLR) approach following the PRISMA 2020 protocol, examining 153 accredited primary education journals (SINTA 1–6) published between 2022 and 2026, of which 16 articles met the inclusion criteria, with data analyzed through thematic coding and descriptive analysis. The results indicate that AI implementation remains at an early stage and is predominantly focused on the development of AI-based teaching media and instructional materials, while the use of adaptive platforms and pedagogical integration remains limited, with emerging trends including personalized learning, the strengthening of literacy and STEM, as well as the incorporation of local contexts. AI implementation has a positive impact on learning outcomes, cognitive skills, motivation, student engagement, as well as learning efficiency and teacher planning, although these effects may be influenced by the novelty effect. Key challenges include infrastructure limitations, teachers’ digital competencies, access disparities, and ethical concerns, while major supporting factors include school leadership and the flexibility of the Merdeka Curriculum. The limitations of this study lie in the limited number of studies and the predominance of descriptive research designs; therefore, future research is recommended to employ more rigorous designs and a broader scope.
Deep Learning and the Love-Based Curriculum in Madrasah Ibtidaiyah: Implications for the Implementation of the Merdeka Curriculum Mohamad Azhar Annawa UBM; Dewi Susilo Reni; Wulan Feronika Maharani
MISOOL: Jurnal Pendidikan Dasar Vol. 8 No. 1 (2026): Juli 2026
Publisher : Program Studi Pendidikan Guru Madrasah Ibtidaiyah Institut Agama Islam Negeri Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47945/misool.v8i1.2781

Abstract

The emergence of the Deep Learning approach and the Love-Based Curriculum (Kurikulum Berbasis Cinta/KBC) amid the implementation of the Merdeka Curriculum has raised fundamental questions among madrasah educators regarding whether these approaches function as replacements for, or complements to, the existing curriculum. This study aims to examine the conceptual role and interrelationship among Deep Learning, the Love-Based Curriculum, and the Merdeka Curriculum in the context of Madrasah Ibtidaiyah education. The study employs a qualitative library research method through a systematic review of literature retrieved from the Google Scholar database, including national and international journal articles and relevant educational policy documents published between 2021 and 2026. The findings indicate that Deep Learning and the Love-Based Curriculum are not substitutes for the Merdeka Curriculum; rather, the three form a synergistic layered curriculum model. The Merdeka Curriculum functions as the national policy foundation that determines educational direction and learning outcomes, Deep Learning serves as an operational pedagogical approach that promotes reflective, meaningful, and engaging learning processes, while the Love-Based Curriculum functions as a value framework and distinctive ethos of madrasah education that cultivates the concept of Panca Cinta, encompassing spiritual, intellectual, social, ecological, and national dimensions. These findings suggest that teachers in Madrasah Ibtidaiyah should integrate all three approaches into a holistic learning ecosystem rather than treating them as separate or alternative options.