<![CDATA[This study presents a systematic literature review (SLR) aimed at synthesizing research findings on the integration of Discovery Learning with local potential to support the development of Computational Thinking (CT) in heat and temperature learning. The review followed Kitchenham’s SLR framework, including the formulation of research questions, search strategy, study selection, and data synthesis. The literature search was conducted using Google Scholar through the Publish or Perish tool with the keywords “Discovery Learning, Local Potential, and Computational Thinking” within the publication range of 2018–2025. A total of 200 articles were initially identified. After the screening process, 20 articles remained, which were further evaluated through eligibility assessment, resulting in 14 relevant studies. Following a full-text review and relevance to Computational Thinking, seven articles were included in the final synthesis. The findings reveal a consistent pattern indicating that Discovery Learning, both independently and when integrated with local potential, contributes to the development of students’ Computational Thinking skills. The integration of local potential enhances contextual understanding and student engagement. In heat and temperature learning, activities such as data analysis, pattern recognition, and experimentation align with key components of CT, including decomposition, abstraction, and algorithmic thinking. However, the review identifies several research gaps, including the limited number of studies explicitly integrating Discovery Learning, local potential, and CT, as well as the lack of structured instructional designs and assessment tools targeting CT indicators. This study highlights the importance of contextualized Discovery Learning as a strategic approach to foster Computational Thinking in science education and recommends further empirical research to strengthen its implementation]]>
Copyrights © 2026
