Profil Literasi Data Siswa pada Pembelajaran IPA Berbasis AI dan Learning Analytics dengan Pendekatan Etnosains

Abstract

Science learning in the era of Artificial Intelligence (AI) and data necessitates the development of students’ data literacy as an essential 21st-century competency. However, students’ data literacy remains underdeveloped, particularly in terms of data analysis and utilization in learning contexts. This study aims to examine students’ data literacy profile in AI-, learning analytics-, and ethnoscience-based science learning. A descriptive quantitative approach was employed, involving eighth-grade junior high school students selected through purposive sampling. Data were collected using a Likert-scale questionnaire validated for reliability (Cronbach’s Alpha = 0.871) and analyzed using descriptive statistics, including mean scores and categorical classification. The results indicate that students’ data literacy was categorized as moderate (mean = 2.92), and learning analytics was also categorized as moderate (mean = 2.91), while artificial intelligence (mean = 3.23) and ethnoscience (mean = 3.23) were categorized as high. These findings suggest that students are able to utilize technology and understand scientific concepts within local contexts; however, their ability to analyze and use data remains underdeveloped. These findings imply the importance of designing science learning in a more structured manner to promote data analysis activities through the contextual and meaningful integration of AI, learning analytics, and ethnoscience.