The Effectiveness of a Deep Learning Oriented Citizen Science Project Learning Model in Improving Students’ Science and Digital Literacy
DOI:
https://doi.org/10.17977/um065.v6.i9.2026.5Keywords:
Citizen science project, Deep learning, Digestive system, Digital literacy, Scientific literacyAbstract
Low levels of science and digital literacy among students pose a challenge in 21st-century education. This study aims to analyze the effectiveness of the Deep Learning-oriented Citizen Science Project (CSP) learning model in improving students’ science and digital literacy. The study employed a quasi-experimental method using a non-equivalent control group pretest-posttest design. The sample consisted of 134 11th-grade students from a public high school, divided into three experimental classes and one control class. Research instruments included a science literacy test and a digital literacy questionnaire. Data analysis was conducted using ANCOVA and Tukey’s HSD post-hoc test. The results showed that the Deep Learning-oriented CSP learning model had a significant effect on improving students’ science literacy (F = 157.422; p < 0.001; ηp² = 0.785) and digital literacy (F = 492.705; p < 0.001; ηp² = 0.920). The results of the study indicate that the Deep Learning-oriented CSP learning model has a significant effect on improving students’ science literacy and digital literacy, with a significance level of p < 0.05. Students in the experimental class demonstrated higher abilities than those in the control class in understanding scientific phenomena, solving problems, and utilizing digital technology in learning. Authentic science-based project-based learning, collaboration, and the use of digital platforms foster meaningful and contextual learning. Thus, the Deep Learning-oriented CSP model is effective in supporting the development of students’ 21st-century competencies, such as science literacy and digital literacy.References
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