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All Journal Jurnal Pijar MIPA International Journal of Technology and Education Research
Bulilingo, Mohamad Rivaldi
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Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Physics Other

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Development of a Science Teaching Module Based on SSCS in the Living Things Classification Concept Bulilingo, Mohamad Rivaldi; Paramata, Dewi Diana; Latjompoh, Masra; Lamangantjo, Chairunnisah J.; Pikoli, Masrid; Nurhayati, Nurhayati
Jurnal Pijar Mipa Vol. 19 No. 4 (2024): July 2024
Publisher : Department of Mathematics and Science Education, Faculty of Teacher Training and Education, University of Mataram. Jurnal Pijar MIPA colaborates with Perkumpulan Pendidik IPA Indonesia Wilayah Nusa Tenggara Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpm.v19i4.7197

Abstract

A teaching module is a learning tool or design based on a curriculum applied to achieve predetermined competency standards. This research aims to use the ADDIE development model to produce a science teaching module based on search, solve, crate and share (SSCS) on valid, practical and effective living things classification concepts. This research was conducted at SMP Negeri 1 Molibagu involving 24 students in Class VII C. The study showed that the results of validating the Science Teaching Module (STM) based on SSCS from experts achieved a score of 90%, which was included in the very valid category and suitable for use in learning. The practicality of the STM based on SSCS as shown by the implementation of learning during two meetings with a percentage of 93% and included in the very good category, student response reached 81.2% with very good criteria, and student activity with a percentage of 85.5% with good criteria. The effectiveness of the STM based on SSCS is shown by the results of the analysis of learning outcomes tests by obtaining N-Gain values 0.78 with high criteria. Thus, it is concluded that the concept of STM based on SSCS in the living things classification concept has valid, practical, and effective criteria. The research of STM based on SSCS is expected to be developed in other material concepts.
The Effect of Artificial Intelligence-Assisted Science Teaching Modules Based on the SSCS Model on Increasing Student Learning Motivation Bulilingo, Mohamad Rivaldi; Uloli, Ritin; Yusuf, Muhammad
International Journal of Technology and Education Research Vol. 3 No. 04 (2025): October - December, International Journal of Technology and Education Research
Publisher : International journal of technology and education research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijeter.v3i04.2639

Abstract

This study aims to examine the effect of an AI-assisted Science module based on the SSCS (Search, Solve, Create, Share) model on students’ learning motivation. A one-group pretest–posttest design was applied to a class of 30 seventh-grade students. Data were collected using a validated Likert-scale questionnaire on learning motivation. Normality tests (Shapiro–Wilk) confirmed that the pretest and posttest scores were normally distributed, allowing the use of a paired sample t-test. The pretest results showed an average learning motivation score of 64.17% (Fair), indicating moderate engagement. Indicators such as willingness and desire to succeed (55%), drive and need to learn (57%), and appreciation in learning (65%) were relatively low, suggesting that students were passive and lacked intrinsic motivation. After implementing the AI-assisted SSCS module, the posttest results demonstrated a significant improvement, with an average score of 79.5% (Good). All indicators increased, including willingness to succeed (80%), drive to learn (77%), and future aspirations (81%, Excellent). The paired t-test confirmed a statistically significant increase in learning motivation (t = 16.23, p < 0.05). The findings indicate that integrating AI with the SSCS learning model enhances students’ engagement, intrinsic motivation, and participation in learning activities. This study suggests that AI-assisted, structured, and interactive learning modules can be an effective strategy to improve both cognitive and affective outcomes in science education.
Co-Authors Chairunnisah J Lamangantjo Dewi Diana Paramata Masra Latjompoh Masrid Pikoli Muhammad Yusuf Nurhayati Nurhayati