- February 10, 2014 @ 10:00 am – 12:00 pm
College of Education, room 608
30 Pryor Street Southwest
Georgia State University, Atlanta, GA 30303
The Effects of Using Visual Statistics Software on Undergraduate Students’ Achievement in Statistics and the Role of Cognitive and Non-Cognitive Factors in Their Achievement
by Kori Lloyd Hugh Maxwell
This study examined the effects of visual statistics software on undergraduate students’ achievement in elementary statistics and the role of cognitive and non·cognitive factors in their achievement. An experimental design as
implemented using ViSta- a visual stalislics program. A sample of 273 undergraduate students at a leading urban southeastern research university enrolled in six sections of Elementary Statistics were selected and randomly assigned to experimental and comparison groups. The participants completed four surveys, with pre- and post-test measures, which assessed their attitudes, statistics self-efficacy, perceptions of their learning environment, and statistical reasoning abilities. To further guide this study, the modified trichotomous framework (Beyth-Marom, Fidler, & Cumming, 2008; Elliot & McGregor, 2001) of goals, cognition, and achievement was used as the theoretical foundation to categorize the cognitive and non-cognitive predictors in relation to student achievement. Three quantitative data analysis methods were utilized. Mann-Whitney tests were employed to determine if there were any statistically significant differences in overall achievement and cognitive and non-cognitive sub-scales between the experimental and comparison groups. Exploratory factor analysis was used to group test items into latent sub-scales for analysis and correlation analysis was used to determine if there were any statistically significant associations between the overall grade in the course and the cognitive and non-cognitive sub-scales. For the qualitative data, error analysis was used to determine any underlying processes or misconceptions evident in students’ problem-solving application. Additionally, reliability analysis determined the internal consistency of the data and fidelity of implementation analysis ensured that the intervention was being applied appropriately. In this study, no statistically significant differences in achievement were noted. However, a significant difference was noted in students’ statistics self-efficacy between the comparison and experimental groups. Finally, using the Pearson product moment correlation (r), a statistically significant correlation was found between the overall grade and attitudes towards the course, attitudes towards statistics in the field, interpreting and applying statistical procedures, identifying scales of measurement, and the negotiation scale of students’ learning environment. Implications of the research results were identified and recommendations were suggested to improve statistics instruction at the undergraduate level.