Changes in Student Attitudes and Understanding within a Statistical Reasoning Learning Environment

1. Changes in Student Attitudes and Understanding within a Statistical Reasoning Learning Environment Daniel Showalter, PhD Assistant Professor of Mathematics, Eastern Mennonite University *Funding provided by the National Science Foundation, DUE-1611713

2. Common outcomes from traditional methods of teaching statistics to non-majors • High levels of anxiety (Chew & Dillon, 2014) • Decreased student interest in statistics (Ramirez, Schau, & Emmioğlu, 2012) • Negative attitudes towards statistics (Murtonen & Lehtinen, 2003) • Neutral to moderate gains in conceptual understanding (McLauchlan & Schonlau, 2016)

3. Why does it matter if students hate statistics or view it as irrelevant? • Avoidance of certain majors, such as psychology (Bourne & Nesbit, 2018) • Exacerbates racial inequities in society (Onwuegbuzie, 1999) • Attitudes towards stats linger much longer than the understanding of content itself (Ramirez, Schau, & Emmioglu, 2012)

4. What can be done to improve attitudes towards statistics? • Discourage procrastination through regular assessment (Chew & Dillon, 2014) • Reduce emphasis on mathematical computations (Chew & Dillon, 2014) • Show the ways in which statistics can help students have a positive impact on their community and society, especially first generation college students (Allen, Smith, Muragishi, Thoman, & Brown, 2015)

5. Statistical Reasoning Learning Environments* emphasize… • Conceptual understanding rather than tools and procedures • Authentic data • Collaborative inquiry-based activities • Technological tools that empower students • Classroom discourse • Alternative assessments * Cobb and McClain (2004)

6. Stimulating Classroom Discourse Student data on relevant issues Sharing personal stories Viewing common experiences through a statistical lens Predictions that force interpretation Statistical terms in contexts that matter Ethical dilemmas Positive impact of stats

7. Textbook Reading Questions to Increase Relevance

9. Nationwide Longitudinal Data Monte Carlo Simulations Global Gender Inequality Spreadsheet Assignments to Empower School-Level Trends Prosperity and the Environment Faith Conversions

10. Authentic Quizzes

11. Research Questions • What changes in affect, value, or interest are observed during the course of a one-semester face-to-face (or online) statistical reasoning learning environment (SRLE)? How do these compare with changes seen at other institutions? • What changes in affect, value, or interest are observed among under-represented minority (URM) students in the face-to-face SRLE? • What changes in affect, value, or interest are observed among first generation students (FGS) in the face-to-face SRLE? • (Exploratory) What other changes in attitude or understanding are observed among specific subgroups?

12. Data • Four statistics classes: two face-to-face (n = 53), two online (n = 30) • Each class given a pretest-posttest on • The Survey of Attitudes Toward Statistics (SATS-36; Schau, 2003); validated likert-scale survey that assesses six attitude components (Affect, Cognitive Competence, Value, Difficulty, Interest, and Effort) and two global components (Math Self-Efficacy, Career Value) • The Comprehensive Assessment of Outcomes in a first Statistics Course (CAOS; Garfield, delMas, & Chance, 2006, http://app.gen.umn.edu/artist/) test; validated 40-question multiple choice test designed to measure students’ understanding of several core statistical concepts • Face-to-face classes also given short essay questions on the relevancy of statistics in their lives (pre/post)

13. Methodology • Pre/post dependent t-tests for various subgroups on each of the six attitudes scales and on the cognitive scale • Exploratory analysis on individual items and for other interesting trends

14. Change Results (face-to-face, n = 53) * All items scored on a 7-pt Likert scale. References as reported in Schauand Emmioglu (2012), n = 2192-2246, who suggest using a threshold of 0.5 change for practical significance.

15. Change Results (online, n = 30) * All items scored on a 7-pt Likert scale. References as reported in Schauand Emmioglu (2012), n = 2192-2246, who suggest using a threshold of 0.5 change for practical significance.

16. Change Results (face-to-face URM, n = 11) * All items scored on a 7-pt Likert scale. References as reported in Schauand Emmioglu (2012), n = 2192-2246, who suggest using a threshold of 0.5 change for practical significance.

17. Change Results (face-to-face first generation, n = 14) * All items scored on a 7-pt Likert scale. References as reported in Schauand Emmioglu (2012), n = 2192-2246, who suggest using a threshold of 0.5 change for practical significance.

18. Mean Gains on Attitude Scales

19. Mean Gains on Attitude Scales (URM/FG)

20. Conceptual Understanding (CAOS) *Reference group had 44.9% correct (pretest) to 54.0% (posttest), a gain of 9.1 percentage points (delMas et al, 2007).

21. Item Analysis: Largest Increased Scores* • Statistics should be a required part of my professional training. (+0.66) • I will (did) enjoy taking statistics courses. (+0.65) • I will (did) like statistics. (+0.45) • Statistical skills will make me more employable. (+0.34) * Average rating change of 53 F2F students on a 7-pt Likert scale shown in parentheses.

22. Item Analysis: Largest Decreased Scores* • I am scared by statistics. (-1.19) • I will have (had) no idea of what’s going on in this statistics course. (-1.09) • I will be (was) under stress during statistics class. (-0.94) • I plan to (did) study hard for every statistics test. (-0.75) * Average rating change of 53 F2F students on a 7-pt Likert scale shown in parentheses.

23. Preliminary Conclusions • Affect(stress, frustration, enjoyment, security, fear) towards stats improved, especially among first generation college students and females. • Statistics were valuedsubstantially more by end of course, especially among underrepresented minority students. Students rated it as more relevant to their lives and careers. • Students didn’t seem to lose interest in using and learning about stats (a major achievement!). • Statistical understanding improved modestly in all groups.

24. References Allen, J. M., Smith, J. L., Muragishi, G. A., Thoman, D. B., & Brown, E. R. (2015). To grab and to hold: Cultivating communal goals to overcome cultural and structural barriers in first-generation college students’ science interest. Translational Issues in Psychological Science, 1(4), 331-341. Bourne, V.J., & Nesbit, R.J. (2018). Do attitudes towards statistics influence the decision to study psychology at degree level? A pilot investigation. Psychology Teaching Review, 24, 55-63. Chew, P.K.H., & Dillon, D.B. (2014). Statistics anxiety update: Refining the construct and recommendations for a new research agenda. Perspectives on Psychological Science, 9(2), 196-208. DOI: 10.1177/1745691613518077. delMas, R., Garfield, J., Ooms, A., & Chance, B. (2007). Assessing students’ conceptual understanding after a first course in statistics. Statistics Education Research Journal, 6(2), 28-58. McLauchlan, C., & Schonlau, M. (2016, December). Are final comments in web survey panels associated with next-wave attrition? In Survey Research Methods (Vol. 10, No. 3, pp. 211-224). Murtonen, M., & Lehtinen, E. (2003). Difficulties experienced by education and sociology students in quantitative methods courses. Studies in Higher Education, 28(3), 171-185. DOI: 10.1080/0307507032000058064. Onwuegbuzie, A.J. (1999). Statistics anxiety among African American graduate students: An affective filter? Journal of Black Psychology, 25(2), 189-209. Ramirez, C., Schau, C., & Emmioglu, E. (2012). The importance of attitudes in statistics education. Statistics Education Research Journal, 11(2), 57-71. Daniel.showalter@emu.edu

25. Pre/Post Means on Attitude Scales (Overall)