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Differences Between Traditional And Non Traditional Students Essay Sample

Differences Between Traditional And Non Traditional Students Pages
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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 statistics anxiety (Pan, W. & Tang, M. 2005). It has also been suggested that non-traditional students (students being of the age of 25 or higher) may suffer from a larger amount of statistics anxiety (Bell, 2003). It has been found that non-traditional students have more commitments and obstacles outside of study than the traditional student (students being under the age of 25), which could impact on their studies. These include family, work and house hold commitments that come before their studies (Hall, 1988, pp 31-32).

Research has shown that 52.6% of the non-traditional students worked more than 21 hours per week, 66.0% travelled further than a distance of five miles, 38.5% recorded themselves as married or living with a long term partner. Compared to the traditional students, non-traditional percentages were much higher on these accounts (Forbus, Newbold, & Mehta, 2011). It was found that as a group non-traditional student’s achieve a lower grade point average when studying than the traditional student(Hall, 1988, pp 31-32). This has was also the case in statistics non-traditional students did worse than traditional students within statistics classrooms, this could be attributed to student’s anxiety towards statistics (Bell, 2003). Non-traditional students having higher levels of statistics anxiety could stem from poor computer efficacy, low levels of computer efficacy have been negatively related to learning computer skills (Harrington, McElroy, & Morrow, 1990).

Surveys have shown that non-traditional students are not as computer savvy as traditional students and use computers less than traditional students (Giacquinta & Shaw, 2000). This could be caused by the lack of exposure they have had to computers through out there schooling years or other areas of their lives. However non-tradition students still viewed the use of computers as a positive, believing that they make work easier and more efficient (Giacquinta & Shaw, 2000). Continuing from this, non-traditional students views are more positive in terms of learning statistics and see more relevance within the subject. Kasworm (1990) believed this to be the cases because they had a greater understanding of what they wanted out of the degree, non-traditional students were studying in a lot of cases to change careers and in other cases from the love of learning. In particular to statistics non-traditional students are far more positive than traditional students.

Traditional students are often only in school because it is expected of them, many are more focused on having a good time (in comparison to non-traditional students) and also many don’t have a clear understanding of what they want out of the degree (Bui & Alfaro, 2011; Hall, 1988). All this suggests that there are differences between non-traditional students and traditional students on how they feel about the area of statistics. Traditional students show to have more computer efficacy and appear to have less anxiety towards statistics, however non-traditional students have a better attitude towards statistics and learning in general (Bell, 2003; Kasworm, 1990; Forbus, Newbold, & Mehta, 2011; Hall, 1988).

It is the aim of the study is to explore the differences between non-traditional and traditional students in the areas of statistics anxiety, attitude towards statistics and computer efficacy. In this study traditional and non-traditional students will be tested by computer surveys at the University of South Australia, which comprises of 173 students enrolled in a statistics course. The three hypotheses being tested are; 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.Method Participants

All participants were University students enrolled in the undergraduate psychology degree and introductory research methods at the University of South Australia. The survey was mandatory and consisted of 173 students’, however two of these students elected not to have their survey answers used in the study (as the information needed consent to be used) leaving 171 surveys for the study. Table 1 show’s that the vast majority of the students were female and that almost half of the non-traditional students had less that year 12 maths experience.

Design
The design was an empirical study (quasi-experiment), which was used to measure anxiety and attitude towards statistics, as well as computer efficacy on target population. The Independent variable was the type of student, being traditional (under 25) or non-traditional (25 and over). The dependent variables were the scales on each of the three areas; anxiety, attitude towards statistics and computer efficacy.

Materials
A questionnaire was used to collect the data from the students and was submitted via Tellus, which is online survey delivery software. The questionnaire consisted of an opening qualitative question asking the participants what they thought studying statistics would involve, followed by three scales. The questionnaire also asked for age, sex, previous maths experience and the final question asked for consent to use the information given. The scales were Statistics Anxiety, Attitude towards Studying Statistics and a Computer Self-Efficacy scale. The Staticstics Anxiety and the Attitude towards Studying Statistics scales were taken from Tremblay, Gardner & Heipel (2000). Each scale consisted of 10 questions, 5 being positively and 5 being negatively worded items on a seven point likert scale which ranged from the answers of strongly agree and strongly disagree.

A high score on the Statistics Anxiety scale indicates high levels of anxiety, an example question taken from the Statistics Anxiety scale is “Statistics class makes me anxious”. Similarly a high score on the Attitude towards Statistics scale indicated a positive attitude, an example of a question from the Attitude scale is “Learning statistics is a valuable and important part of truly understanding psychology”. The Computer Self-Efficacy scale consisted of 20 questions, all of which were positively worded and were measured on a five point likert scale ranging from strongly agree to strongly disagree. The scale was adapted from Tremblay, Gardner & Heipel (2000)’s 29 item scale. A high score on the Computer Self-Efficacy scale indicated a high level of computer self-efficacy, an example taken from this scale was “I feel confident working on a personal computer”.

Procedure
The participants from the study completed the questionnaire at their first practical class for the semester and were instructed by the tutor where to find the link to complete the questionnaire. The class sizes ranged from 20 to 30 people and the classroom held 20 available computers. The participants were given greater understanding of how this information was going to be used in future classes. Results

Data were entered into SPSS v20 and screened for normality. The Statistics Anxiety and Computer Self Efficacy scales were normally distributed. The Attitude to Studying Statistics scale was significantly negatively skewed however was normalised after removal of two outliers. Age was significantly positively skewed and could not be normalised. Descriptive statistics are shown in Table 2 below.

Table 2.
Results for the Three Scales Tested

Traditional
NonTraditional
Total
Statistics Anxiety

Mean=42.73
SD=10.71
n=141
Mean=43.50
SD=11.04
n=30
Mean=42.87
SD=10.74
N=171
Attitude To
Learning Statistics

Mean=49.74
SD=7.04
n=141
Mean=54.13
SD=9.24
n=30
Mean=50.51
SD=7.63
N=171
Computer
Self-Efficacy

Mean=81.73
SD=11.43
n=141
Mean=75.93
SD=17.60
n=30
Mean=80.7
SD=12.86
N=171

The non-traditional student scored higher on the statistics anxiety scale than the traditional student, an independent samples t-test revealed that the difference was not significant, t(169)=.355, p=0.362 (one-tailed), d=0.071. A second independent t-test revealed that, as predicted, non-traditional students scored significantly higher on attitude towards learning statistics than traditional students, Levines test of equality of variances was significant (p=0.044), t(36.5)=2.46, p=.0095 (one-tailed), d=0.54. A final independent t-test revealed that, as predicted, that traditional students scored significantly higher on the computer self-efficacy scale than non-traditional students, Levines test of equality of variances was significant (p=0.001), t(34.38)=1.728, p=0.0465(one-tailed), d=0.39.

Discussion
Results for the statistics anxiety scale showed that non-traditional students did score higher on the scale as compared to traditional students. However an independent sample t-test showed that the difference we negligible, as the scores gained from the test scale showed the two to be extremely close. With these results we reject our hypothesis that the non-traditional student have more statistics anxiety than the traditional student, the results of this study also goes against James Bell’s research into statistics anxiety which found that non-traditional students do have an increased level of anxiety (Bell, 2003). Similar to our findings Kasworm (1990) found that Adult undergraduates (non-traditional students) were believed to have more anxiety when studying at a higher level of education, but in fact just as our results show the difference is negligible. Suggesting there could be misconceptions about the modern non-traditional student. Dornan & Justice (2001) also found that even though non-traditional students face more pressures outside of study such as career demands and family pressures, they achieve results comparable to traditional students in the same area of study.

Further supporting these results Forbus, Newbold, & Mehta (2011) have also found there to be little to no difference in traditional and non-traditional students anxiety towards higher levels of study. The results found in the attitude towards learning statistics scale showed that non-traditional students do in fact have a better attitude towards learning statistics and see more value in what is taught within a statistics course. The results showed a significant difference and thus the hypothesis that non-traditional students will score higher in the attitudes towards statistics scale as compared to traditional students is accepted. Richard Hall wrote in his comparative study on traditional and non-traditional students, “Regarding motives for college attendance, non-traditional students appeared to have a clearer vision, both in terms of specificity and immediacy” (Hall, 1988, p. 25).

He also continues in saying that non-traditional students motives to learn and come back to study is strengthened by real life experiences (Hall, 1988). Non-traditional students appear to have a greater positive attitude towards statistics and study at higher levels of education in general, they have direct reasons for study which has stemmed from life experience. Lastly the results for computer self-efficacy scale has supported that traditional student’s do have better computer self-efficacy. Once again significance was shown between the results for traditional and non-traditional students, supporting the hypothesis that traditional students will score higher on the computer self-efficacy scale as compared to traditional students. This has been attempted to be explained though non-traditional students lack of computer usage throughout schooling phases of their lives, as well as generally less usage in day to day life (Giacquinta & Shaw, 2000).

The study was a good starting point for looking at the differences between traditional and non-traditional students, it has shown reasonable cause to continue research to find further variances between the two types of students. This study however was lacking in terms of the finer details, not much research was put into finding the underlying causes for traditional and non-traditional students scoring as they did on the three scales tested. Several factors could have also influenced the results of the questionnaires, social desirability could have been a factor. When completing the survey many students had to share a computer, this could have changed answers given. For future research it would be beneficial to look at reasons why there could be possible statistics anxiety in students (not just looking at difference between traditional and non-traditional students) and also looking at why traditional students don’t have a positive attitude towards learning statistics. Positively this study has shed light on a misconception that non-traditional students struggle and have more anxiety towards statistics or study in a general sense.

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. A. (2003). Statistics anxiety: The nontraditional student. Education, 124(1), 157-162. 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. E. (1990). Adult Undergraduates in Higher Education: A Review of Past Research perspectives. REVIEW OF EDUCATIONAL RESEARCH. pp 367. 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. N., & Gardner, R. C. (1993). Statistics as a second language? A model for predicting performance in psychology students. Canadian Journal Of Behavioural Science/Revue Canadienne Des Sciences Du Comportement, 25(1), 108-125. 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. Newbold, J. J., Mehta, S. S. & Forbus, P. (2010). A comparative study between non-traditional and traditional students in terms of their demographics, attitudes, behavior and educational performance. International Journal Of Education Research, 5(1), 1-24. 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. (2005). Students’ Perceptions on Factors of Statistics Anxiety and Instructional Strategies. Journal of Instructional Psychology. Pp 209-212. 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. Tremblay, P. F., Gardner, R. C. & Heipel, G. G. (2000). A model of the relationships among measures of affect, aptitude, and performance in introductory statistics. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement, 32(1), 40-48. Appendix

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