University science education students’ performance in processing of a scientific data set

TeMost empirical studies in science education on student processing of scientific data have reported that most elective science students studying physics do not understand how to use the term 'repeat' in a dataset or identify anomalies in scientific measurements. it appears that not many studies have focused on university students in Ghana. Therefore, this study would bridge the gap by concentrating on university science education students’ performance in processing a scientific data set. The study employed a cross-sectional survey design in collecting data on using repeat (UR) and anomalies in the dataset (AN) of processing of data in a scientific dataset. The sample of the study was 220 out of 495 science education students. Data was collected using a questionnaire. The findings were that on using the repeat (UR) data set item, 13.60% of students had the right response, and on the anomaly (AN) data set item, 9.10% of students had the right response. With this, it appears the majority of science education students have challenges in processing data of a scientific dataset. Also, the female students (16.7% on the UR item and 10.00% on the AN item) had more right responses than their male (10.00% on the UR item and 8.00% on the AN item) counterpart. This implies that descriptively female science education students performed better compared to the male science education students. ANCOVA was conducted to examine whether there were significant differences between male and female science education students in their ability to process scientific data using repeated (UR) and identify anomalies (AN) within a scientific dataset. 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. The effect size was large (η² = .748 and .804), suggesting that approximately 74.8% of the variance in UR scores and 80.4% of the variance in AN scores could be attributed to students’ sex. A post hoc comparison using Tukey’s test for both UR and AN revealed that female students significantly outperformed their male counterparts, with a mean difference of -2.63 and -3.20, respectively. The negative mean difference confirms that male students had significantly lower scores on both the UR and AN tasks than their female counterparts. It is recommended that conscious data processing skills should be mounted for science education students.