Pengembangan E-modul Kimia Berbasis PBL dan Deep Learning pada Materi Termokimia
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Penelitian ini bertujuan untuk mengembangkan e-modul kimia berbasis Problem-Based Learning (PBL) yang terintegrasi dengan pendekatan deep learning pada materi termokimia serta menganalisis kelayakan, kepraktisan, dan efektivitasnya. Penelitian ini menggunakan metode Research and Development dengan model 4D (Define, Design, Develop, Disseminate). Subjek penelitian adalah 32 siswa kelas XI SMA Negeri 17 Medan yang dipilih secara purposive sampling karena mewakili kemampuan akademik heterogen dan sesuai kebutuhan uji coba produk. Instrumen penelitian meliputi lembar validasi ahli, angket respons siswa, serta tes hasil belajar pretest-posttest. Hasil penelitian menunjukkan e-modul berada pada kategori sangat layak untuk aspek media (86%) dan layak untuk aspek materi (73,38%). Respons siswa menunjukkan kategori praktis, sedangkan efektivitas ditunjukkan oleh nilai N-gain 0,71 (kategori tinggi). Temuan ini menegaskan bahwa integrasi PBL dan deep learning dalam bahan ajar digital mampu memperkuat pembelajaran bermakna, keterlibatan siswa, dan pemahaman konseptual pada materi kimia.
Development of a PBL- and Deep Learning-Based Chemistry E-Module on Thermochemistry
This study aims to develop a chemistry e-module based on Problem-Based Learning (PBL) that integrates a deep learning approach into thermochemistry material, as well as to analyze its feasibility, practicality, and effectiveness. This study employs the Research and Development method using the 4D model (Define, Design, Develop, Disseminate). The subjects of the study were 32 eleventh-grade students at State High School 17 Medan, selected via purposive sampling because they represented a heterogeneous academic ability and met the requirements for product testing. Research instruments included an expert validation sheet, a student response questionnaire, and pretest-posttest learning outcome tests. The results of the study indicate that the e-module falls into the “highly suitable” category for the media aspect (86%) and the “suitable” category for the content aspect (73.38%). Student responses indicate the “practical” category, while effectiveness is demonstrated by an N-gain value of 0.71 (high category). These findings confirm that the integration of PBL and deep learning in digital instructional materials can enhance meaningful learning, student engagement, and conceptual understanding of chemistry content.
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