MEASURING SCIENTIFIC DATA PROCESSING SUCCESS AMONG UNIVERSITY SCIENCE EDUCATION STUDENTS

Purpose: This study aims to explore university science education students’ performance in processing a scientific data set and to determine which gender performs better in processing a scientific data set. Design/Methodology/Approach: The study used a descriptive survey approach to give a vivid description of the data collected on university science education students’ performance in processing a scientific data set. The study included 220 students in science education. 44% of the sample from each population group was randomly selected. The data was collected using a questionnaire during the second semester of the 2023/2024 academic year. The data were analysed using the Jamovi statistical package (i.e., percentages and ANCOVA statistics). Findings: Using the repeat (UR) data set item, 13.60% of students had the correct response, and using the anomaly (AN) data set item, 9.10% of students had the correct response. Also, the female students (16.7% on the UR item and 10.00% on the AN item) had more correct responses than their male counterparts (10.00% on the UR item and 8.00% on the AN item). The overall model was statistically significant, F(1,218) = 646.00, p < .001 and F(1,218) = 895.00, p < .001, respectively, indicating that gender had a meaningful impact on students’ performance in the UR and AN tasks. Research Limitation: Some students were absent during data collection. The findings of this study cannot represent all students' performance. Generalisation of the results should be done with caution. Practical Implication: This study suggests that science educators can improve their teaching by using real-world data, incorporating technology for data analysis, and enhancing "hands-on, mindon" practical work to make learning more engaging and relevant. Social Implication: This study suggests fostering knowledgeable citizens by developing proficiency in scientific data analysis, thereby extending critical thinking skills beyond the classroom. Originality: This study contributes to the growing body of literature in science education on students' processing of datasets in scientific measurement.