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Analisis Kebutuhan Pengembangan Bahan Ajar Kesetimbangan Kimia Berbasis Etnosains Terintegrasi Ekosistem Mangrove Yuni Kurnia; Yuli Hartati; Muflihah Muflihah; Farah Erika
SAP (Susunan Artikel Pendidikan) Vol. 11 No. 1 (2026): SAP
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/sap.v11i1.1804

Abstract

This study aims to analyze students' needs as a basis for developing ethnoscience-based chemistry teaching materials on chemical equilibrium by integrating the context of the mangrove ecosystem in Paser Regency, East Kalimantan. The study used a quantitative descriptive approach with a survey design of 40 grade XI students at SMA Negeri 1 Tanah Grogot through a Likert scale questionnaire compiled based on 13 indicators and grouped into four dimensions, namely the relevance of teaching materials, conceptual understanding, learning experience and technology involvement, and the integration of ethnoscience and local wisdom. The research findings indicate that the teaching materials used are still abstract and have not optimally accommodated the local context, while students show a high need for additional teaching materials and learning related to the mangrove ecosystem.. This study contributes to the development of ethnoscience-based chemistry education by demonstrating the potential of mangrove ecosystems as a local context for learning chemical equilibrium. The empirical evidence obtained provides a foundation for the development of ethnoscience-based teaching materials that can connect abstract chemical concepts with local environmental phenomena close to students' lives.
Pengembangan Modul Ajar Diffrensiasi Berbasis Deep Learning Untuk Meningkatkan Hasil Belajar Pada Materi Sistem Periodik Unsur Di SMAN 1 Sangatta Selatan Rosiana Pakiding; Mukhamad Nurhadi; Yuli Hartati
Journal of Creative Student Research Vol. 4 No. 4 (2026): Agustus: Journal of Creative Student Research
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsr-politama.v4i4.6485

Abstract

This study aimed to develop a differentiated teaching module based on the Deep Learning approach for the topic of the Periodic Table of Elements and to determine its development process, validity, practicality, and effectiveness in improving students' learning outcomes. The research employed a Research and Development (R&D) method using the 4-D development model, which consists of the Define, Design, Develop, and Disseminate stages. However, this study was limited to the Develop stage. The trial subjects were students of Class X-6. Data were collected using validation sheets to evaluate the module's validity, teacher and student response questionnaires to assess its practicality, and learning achievement tests to measure its effectiveness. The collected data were analyzed using descriptive qualitative and quantitative methods. The results showed that the development process began with an analysis of students' characteristics, including learning readiness, interests, and learning styles, followed by the integration of differentiated instruction elements (content, process, and product) with the principles of the Deep Learning approach (mindful, meaningful, and joyful learning). The teaching module was categorized as highly valid based on the evaluations of media, language, and subject-matter experts. It was also considered highly practical, as indicated by positive responses from teachers 94% and students, with practicality scores of 88%. Furthermore, the module proved to be effective in significantly improving students' learning outcomes, as demonstrated by the increase in the average post-test score, an N-Gain value of 0.74, which falls into the high category, and an effect size of 3.53, indicating a very large effect. These findings suggest that the differentiated teaching module based on the Deep Learning approach effectively enhances students' learning outcomes. In conclusion, the differentiated teaching module based on the Deep Learning approach for the topic of the Periodic Table of Elements is valid, practical, and effective for use in chemistry learning.