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Science education in elementary schools requires the active involvement of students through meaningful learning experiences. One of the essential subjects in fourth grade elementary school is the growth and development of animals and plants. However, science education is still often conducted conventionally, so that students' scientific process skills have not developed optimally. This study aims to analyze and describe the application of the Project-Based Learning (PjBL) model in science learning projects on the growth and development of animals and plants with the support of Seesaw and Flashcard Quizlet digital media through a Systematic Literature Review (SLR) approach. The research method used SLR with a descriptive qualitative approach to relevant scientific articles published between 2019 and 2025. The results of the study show that the application of PjBL can increase student learning activity, scientific process skills, and understanding of science concepts. The use of Seesaw was effective as a medium for project documentation and reflection, while Flashcard Quizlet helped reinforce concepts and formative evaluation. Thus, the integration of PjBL, Seesaw, and Quizlet can be an innovative learning alternative that is relevant to the Merdeka Curriculum and the needs of elementary school students.

The increasing use of Learning Management Systems (LMS) in higher education generates large amounts of student activity data that have the potential to provide deeper insights into learning processes. However, in practice, these data are still rarely analyzed systematically to understand variations in students’ learning activity patterns, limiting their practical use in supporting teaching and learning. This study aims to explore students’ learning activity patterns in an LMS using a clustering approach based on activity data.This research utilizes the publicly available Open University Learning Analytics Dataset (OULAD), focusing on a single course and a single academic term. LMS activity data were processed through data cleaning and feature extraction, followed by student clustering using the K-Means algorithm. The quality of the clustering results was evaluated using the Silhouette Score, and visual analysis was applied to support the interpretation of the results.The results indicate that students’ learning activities can be grouped into two main patterns, namely a group of students with high learning activity and a group with lower or moderate activity levels. These findings highlight the existence of heterogeneous learning behaviors among students, even within the same learning context.The identified learning activity patterns provide an initial foundation for utilizing LMS data to monitor student engagement and to support the development of more responsive, data-driven learning approaches in higher education.