Designing Stoichiometry Learning with Multimedia Personalized Voice and Computational Thinking: Insights from a Mobile App
List of Authors
Mimi Malini Mohmad Fuzi, Nurullizam Jamiat, Wan Ahmad Jaafar Wan Yahaya
Keyword
Stoichiometry, Multimedia Personalized Voice, Computational Thinking, Mobile Application, Learning Design
Abstract
In the era of rapid technological advancement and increasing demand for effective Science, Technology, Engineering, and Mathematics (STEM) education worldwide, there is a pressing need to innovate chemistry learning, particularly in challenging topics such as stoichiometry for pre-university students. This study explores chemistry lecturers’ perspectives on integrating Multimedia Personalized Voice and Computational Thinking (CT) in designing stoichiometry learning through a mobile application designed for pre-university students. Employing a qualitative research approach with a basic qualitative inquiry design, data were collected via interviews with five experienced chemistry lecturers. Thematic analysis revealed that personalized voice multimedia enhances student engagement and reduces cognitive load, while CT supports structured and systematic problem-solving in stoichiometry. Despite technical and pedagogical challenges, the developed learning design model shows promise in improving students’ conceptual understanding and problem-solving skills. This study contributes to the development of interactive and relevant learning models aligned with contemporary chemistry education needs. The findings provide valuable guidance for educators and educational app developers aiming to enhance the quality of stoichiometry teaching and learning at the pre-university level.