UNDERSTANDING UNIVERSITY SCIENCE EDUCATION STUDENTS' REASONING IN SCIENTIFIC DATASET PROCESSING

Purpose: This study investigates how university science education students reason about scientific data and whether there are gender differences in this reasoning. Design/Methodology/Approach: A cross-sectional survey design was employed to gather data. A simple random sampling technique was used in this study. The study sampled 220 science education students using an open-ended questionnaire in the Department of Teacher Education at the University of Ghana. An Analysis of Covariance (ANCOVA) was employed to identify significant differences in the reasoning of male and female students in the processing of scientific datasets. Findings: Results indicated that 22.7% of students demonstrated a novice mindset regarding the anomaly (AN) issue, with another 22.7% exhibiting a mixed novice/expert mindset. For the using repeat (UR) question, 45.5% displayed a novice mindset, and 4.5% a mixed mindset. Gender significantly affected students' performance on both the AN item F (1,218) = 229.00, p < .001, η² = .512) and UR item (F(1,218) = 113.00, p < .001, η² = .342) tasks. The model was statistically significant for both tasks, with substantial effect sizes indicating that gender accounted for 51.2% and 34.2% of the variance in AN and UR scores, respectively. Research Limitation: The findings of this study cannot represent all students' performance. Generalisation of the results should be done with caution. Practical Implication: This knowledge can improve science education, teaching methods, and assessments, ultimately fostering better data analysis skills and conceptual understanding in future scientists and informed citizens. Social Implication: It is significant because it promotes scientific literacy and critical thinking, enabling informed decision-making and fostering a more knowledgeable society capable of interpreting evidence in real-world contexts. Originality: This study contributes to the growing body of literature in science education on students’ reasoning about scientific data in scientific measurement.