<![CDATA[The rapid advancement of digital technology has transformed various aspects of human life, including education. Educational institutions are expected not only to develop students’ academic competencies but also to prepare them to utilize technology effectively, critically, and responsibly in learning activities. Therefore, the integration of digital technology into science education has become increasingly important to support the development of students’ twenty-first-century skills.This study aimed to develop a temperature and heat learning module based on Visual Programming and Machine Learning with a Structured Inquiry learning model and to determine its validity, teachers’ and students’ perceptions, and students’ conceptual understanding after using the developed module. This research employed a Research and Development (R&D) method using the 4D model. Data were collected through material and media expert validation sheets, teacher and student perception questionnaires, and conceptual understanding tests. The results showed that the module obtained a validity score of 95.19% from material experts and 92.50% from media experts, both categorized as highly valid. Teacher and student perception scores reached 88.37% and 81.99%, respectively, indicating very good perceptions of the developed module. Students’ conceptual understanding increased from an average pre-test score of 47.07 to 79.48 on the post-test, with an N-Gain score of 0.61 in the medium category. In addition, the achievement of conceptual understanding indicators was categorized as high and very high. These findings indicate that the developed module is highly valid, receives very good responses from teachers and students, and supports the improvement of students’ conceptual understanding of temperature and heat concepts.]]>
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