Differences Between Traditional And Non Traditional Students

Running head: Differences in a Statistics Based Classroom

Differences between Traditional and Non-traditional Students in a Statistics Based Classroom

Abstract
This report examines the differences between traditional and non-traditional students in terms of three aspects; anxiety towards statistics, attitude towards statistics and computer self-efficacy. A review of literature was conducted and hypotheses were formed about the three aspects. The three hypotheses tested were and what was expected to be found was; traditional students will score lower on the statistics anxiety scale as compared to non-traditional students, non-traditional students will score higher in the attitudes towards statistics scale as compared to traditional students and lastly traditional students will score higher on the computer self-efficacy scale as compared to traditional students. The study was conducted on a statistics rich course at a university level, a questionnaire was used on 173 students to gather the information. It was found that the first hypothesis was rejected, as non-traditional students don’t have more statistics anxiety. The other two hypotheses were accepted. This suggests misconceptions about non-traditional students struggling more at higher levels of study than traditional students.
Differences between Traditional and Non-traditional Students in a Statistics Based Classroom

Statistics anxiety is prevalent for most students and can often impact on a students learning in the classroom. Many factors can play a role in affecting a student’s anxiety and attitude within a classroom, it has been found that a lack of mathematical skills or knowledge can make statistics daunting, inadequacy to connect what is learnt in statistics to everyday life can cause some students to believe it to be irrelevant, the pace of the statistics class can be too fast for some students and the attitude of the instructor in the classroom all can be contributions to a students

References: Ballantyne, J., Madden, T. & Todd, N. (2009). Gauging the attitudes of non-traditional students at a new campus: an Australian case study, Journal of Higher Education Policy and Management, 31(4), 301-313. Baloglu, M. (2003). Individual Differences in Statistics Anxiety among College Students. Personality and Individual Differences, 34, 855-865. Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory, Englewood Cliffs, N.J.: Prentice-Hall.  Bell, J Bui, N. A. & Alfaro, M. A. (2011). Statistics anxiety and science attitudes: age, gender, and ethnicity factors. College Student Journal, 45(3), 573-585. Cruise, R. J., Cash, R. W. & Bolton, D. L. (1985). Development and validation of an instrument to measure statistical anxiety. Paper presented at the annual meeting of the American Statistical Association Statistics Education Section. Las Vegas, Nevada. Compton, J. I., Cox, E., & Laanan, F. S. (Summer 2006). Adult Learners in Transition. New Directions for Student Services, 114, 73 – 80. Devonport, T. M. (2006). RELATIONSHIPS BETWEEN SELF-EFFICACY, COPING AND STUDENT RETENTION. Social Behavior & Personality: An International Journal, 34(2), 127-138. Dykeman, B. F. (2011). Statistics anxiety: antecedents and instructional interventions. Education, 132(2), 441-446. Forbus, P; Newbold, J; Mehta, S. (2011). Academy of Educational Leadership Journal, suppl. Special Issue15. pp 109-125. Giacquinta, J. B;  Shaw, F. S. (2000) A survey of graduate students as end users of computer technology: New roles for faculty. Information Technology, Learning, and Performance Journal,18. 1. Hall, Richard Eugene. (1988). Adults on traditional campuses: A comparative study of non traditional and traditional students. The University of Oklahoma. pp 31-32. Harrington, K. V., McElroy, J. C., & Morrow, P. C. (1990). Computer anxiety and computer-based training: A laboratory experiment. Journal of Educational Computing Research, 6, 343-358. Hutchison, M. A., Follman, D. K., Sumpter, M. and Bodner, G. M. (2006). Factors Influencing the Self-Efficacy Beliefs of First-Year Engineering Students. Journal of Engineering Education, 95(1),39-47. Dornan, T. M. &. Justice, E. M (2001). Metacognitive Differences Between Traditional-Age Students and Nontraditional-Age College Students. Adult Education Quarterly, 51, 236-249. Kasworm, C Keeley, J., Zayac, R. & Correia, C. (2008). Curvilinear relationships between statistics anxiety and performance among undergraduate students: evidence for optimal anxiety. Statistics Education Research Journal, 7(1), 4-15.  Lalonde, R Macher, D., Paechter, M. , Papousek, I. & Ruggeri, K. (2011). Statistics anxiety, trait anxiety, learning behavior, and academic performance. European Journal of Psychology of Education, 1-16. Onweugbuzie, A. J. (2000). Statistics Anxiety and the Role of Self-Perceptions. The Journal of Educational Research, 93 (5), 323-330. Onwuegbuzie, A. J. & Wilson, V. A. (2003). Statistics Anxiety: Nature, etiology, antecedents, effects, and treatments--a comprehensive review of the literature, Teaching in Higher Education, 8(2), 195-209.  Pan, W., Tang, M Ruggeri, K., Dempster, M. & Hanna, D. (2011). The impact of misunderstanding the nature of statistics. Psychology Teaching Review, 17(1), 35–40. Sam, H. K., Othman, A. E. A. & Nordin, Z. S. (2005). Computer Self-Efficacy, Computer Anxiety, and Attitudes toward the Internet: A Study among Undergraduates in Unimas. Educational Technology & Society, 8 (4), 205-219.