Exploring Achievement Behaviors in Non-Major Statistics Course: An Expectancy-Value Perspective and Thoughts for Practice
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Abstract
Statistics education is increasingly important to our society with enrolment increases of 16% in introductory statistics courses and 85% in upper-level statistics courses. Research has demonstrated many factors related to students’ behaviors and outcomes in statistics courses such as past achievement, attitudes, and effort. We sought to model these factors together to better understand how introductory statistics students’ attitudes were related to students' achievement behaviors and what student characteristics mediated such relationships. Structural equation modeling with data from N=301 students in an introductory statistics course for psychology majors revealed that majors with higher GPAs had more interest, enjoyment as well as utility value for statistics, and these variables were in turn related to expectations for success or achievement behaviors. Females had lower interest in statistics, and this was related to lower expectations of success. The findings highlight the need to increase interest and enjoyment and utility value for non-majors studying statistics. Recommendations for how to adapt the statistics classroom to that end are discussed.
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References
Acee, T. W., & Weinstein, C. E. (2010). Effects of a value-reappraisal intervention on statistics students' motivation and performance. Journal of Experimental Education, 78(4): 487–512. https://doi.org/10.1080/00220970903352753
American Psychological Association. (2014). APA guidelines for the undergraduate psychology major: Version 2.0. Author.
American Statistical Association. (2012). Guidelines for Assessment and Instruction in Statistics Education: College Report. www.amstat.org/asa/files/pdfs/GAISE/GaiseCollege_Full.pdf
Beyer, A. & Peters, A. (2020). For the love of teaching undergraduate statistics. Retrieved from the Society for the Teaching of Psychology web site: http://teachpsych.org/ebooks/
Bornholt, L. J. (2001). Self-concepts, usefulness and behavioral intentions in the social context of schooling. Educational Psychology, 21(1), 67-78. http://dx.doi.org/10.1080/01443410020019849
Cano, F., Pichardo, M. C., Berbén, A. B. G., & Fernández-Cabezas, M. (2021). An integrated test of multidimensionality, convergent, discriminant and criterion validity of the course experience questionnaire: An exploratory structural equation modelling. Assessment and Evaluation in Higher Education, 46(2): 256-268. http://dx.doi.org/10.1080/02602938.2020.1771278
Carlson, K., & Winquist, J. (2011). Evaluating an active learning approach to teaching introductory statistics: A classroom workbook approach. Journal of Statistics Education, 19(1): 1-23. doi:10.1080/10691898.2011.11889596
Cashin, S. E., & Elmore, P. B. (2005). The Survey of Attitudes Toward Statistics Scale: A construct validity study. Educational and Psychological Measurement, 65(3), 509–524. http://doi.org/10.1177/0013164404272488
Chiesi, F., & Primi, C. (2010). Cognitive and non-cognitive factors related to students’ statistics achievement. Statistics Education Research Journal, 9(1): 6–26. http://www.stat.auckland.ac.nz/~iase/serj/SERJ9%281%29_Chiesi_Primi.pdf
Clay, R. A. (2017). Women outnumber men in psychology, but not in the field’s top echelons. Monitor on Psychology, 48(7): 18.
Corwyn, R. F., & McGarry, P. P. (2020). An expectancy value theory predicts achievement in undergraduate statistics through academic delay of gratification. Statistics Education Research Journal, 19(2): 42-56.
Dai, T., & Cromley, J. G. (2014). Changes in implicit theories of ability in biology and dropout from STEM majors: A latent growth curve approach. Contemporary Educational Psychology, 39(3): 233–247. https://doi.org/10.1016/j.cedpsych.2014.06.003.
Dunn, D. S., Smith. R. A., Beins, B. C. (2007). Best practices for teaching statistics and research methods in the behavioral sciences. Lawrence Erlbaum Associates.
Eccles, J. S., Adler, T., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation: Psychological and sociological approaches (pp. 76–146). W. H. Freeman and Company. http://doi.org/10.1207/s15327752jpa8502
Emmioğlu, E., & Capa-Aydin, Y. (2012). Attitudes and achievement in statistics: A meta-analysis study. Journal of Educational and Behavioral Statistics, 11(2): 95–102.
GAISE College Report ASA Revision Committee (2016). Guidelines for assessment and instruction in statistics education college report 2016. http://www.amstat.org/education/gaise.
Goldfinch, J. (1996). The effectiveness of school-type classes compared to the traditional lecture/tutorial method for teaching quantitative methods to business students. Studies in Higher Education, 21(2): 207–220.
Green, T. (2016). A methodological review of structural equation modeling in higher education research. Studies in Higher Education, 41(12): 2125-2155. http://dx.doi.org/10.1080/03075079.2015.1021670
Han, H., Bong, M., Kim, S., & Kwon, S. K. (2019). Utility value and emotional support of teachers as predictors of student utility value and achievement. Educational Psychology, http://dx.doi.org/10.1080/01443410.2019.1693509
Hood, M., Creed, P. A., & Neumann, D. L. (2012). Using the expectancy value model of motivation to understand the relationship between student attitudes and achievement in statistics. Statistics Education Research Journal, 11(2): 72–85. https://doi.org/10.52041/serj.v11i2.330
Hilton, S. C., Schau, C., & Olsen, J. A. (2004). Survey of Attitudes Toward Statistics: Factor structure invariance by gender and by administration time. Structural Equation Modeling, 11(1), 92–109. http://doi.org/10.1207/S15328007SEM1101_7
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. http://doi.org/10.1080/10705519909540118
Johnson, M. L., Edwards, O. V., & Dai, T. (2014). Growth trajectories of task value and self-efficacy across an academic semester. Universal Journal of Educational Research, 2(1): 10-18. DOI: 10.13189/ujer.2014.020102
Kyttala, M. & Bjorn, P. M. (2010). Prior mathematics achievement, cognitive appraisals, and anxiety as predictors of Finnish students’ later mathematics performance and career orientation. Educational Psychology, 30(4), 431-448. http://www.informaworld.com/openurl?genre=article&id=doi:10.1080/01443411003724491
Lawson, T. J., Schwiers, M., Doellman, M., Grady, G., & Kelnhofer, R. (2003). Enhancing students’ ability to use statistical reasoning with everyday problems. Teaching of Psychology, 30(2): 107-110. doi:10.1207/S15328023TOP3002_04
Lai, B. S., Livings, M. S., D’amico, M. P., Hayat, M. J., & Williams, J. (2018). A growth mindset pilot intervention for a graduate-level biostatistics course. Statistics Education Research Journal, 17(2): 104-119. http://www.stat.auckland.ac.nz/serj
Marsh, H. W., Muthén, B. O., Asparouhov, T., Lüdtke, O., Robitzsch, A., & Morin, A. S., et al. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students’ evaluations of university teaching. Structural Equation Modeling, 16(3), 439–476. http://dx.doi.org/10.1080/10705510903008220
Murtonen, M., & Lehtinen, E. (2003). Difficulties experienced by education and sociology students in quantitative methods courses. Studies in Higher Education, 28(2): 171–185. https://doi.org/10.1080/0307507032000058064
Muthén, L. K., & Muthén, B. O. (n.d.). Mplus user’s guide (6th edition). Muthén & Muthén.
National Science Foundation. (2018). NSF’s 10 big ideas. https://www.nsf.gov/news/special_reports/big_ideas/harnessing.jsp
Nguyen, T. H., Charity, I. & Robson, A. (2016). Students' perceptions of computer-based learning environments, their attitude towards business statistics, and their academic achievement: Implications from a UK university. Studies in Higher Education, 41(4): 734-755. http://dx.doi.org/10.1080/03075079.2014.950562
Paunesku, D., Walton, G. M., Romero, C., Smith, E. N., Yeager, D. S., & Dweck, C. S. (2015). Mind-Set interventions are a scalable treatment for academic underachievement. Psychological Science, 26(6): 784-793. doi: 10.1177/0956797615571017
Perez, T., Dia, T., Kaplan, A., Cromley, J. G., Brooks, W. D., White, A. C., Mara, K. R., & Balsai, M. J. (2019). Interrelations among expectancies, task values, and perceived costs in undergraduate biology achievement. Learning and Individual Differences, 72, 26-38. https://doi.org/10.1016/j.lindif.2019.04.001
Ramirez, C., Schau, C., & Emmioǧlu, E. (2012). The importance of attitudes in statistics education. Statistics Education Research Journal, 11(2), 57–71. https://iase-web.org/documents/SERJ/SERJ11(2)_Ramirez.pdf
Schau, C. (2003). Students’ attitudes: The “other” important outcome in statistics education. In Joint Statistical Meetings (pp. 3987–3992). Alexandra, VA: American Statistical Association. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.6628&rep=rep1&type=pdf
Schau, C., Stevens, J., Dauphinee, T. L., & Del Vecchio, A. (1995). The development and validation of the Survey of Attitudes Toward Statistics. Educational and Psychological Measurement, 55(5), 868–875. http://doi.org/10.1177/0013164495055005022
Schoenfelder, E., Olson, R., Bell, M., & Tom, K. (2007). Stop and smell the roses: An activity for teaching the Central Limit Theorem. Psychology Learning and Teaching, (2): 80-84. doi:10.2304/plat.2007.6.2.80
Smith, T. (2017). Gamified modules for an introductory statistics course and their impact on attitudes and learning. Simulation & Gaming, 48(6), 832-854. https://doi.org/10.1177/1046878117731888
Smith, T., & Capuzzi, G. (2019). Using mindset interventions to improve outcomes in statistics courses. Psychology Teaching and Learning, 18(3): 326-336.
Sorge, C., & Schau, C. (2002). Impact of engineering students’ attitudes on achievement in statistics. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, April. http://evaluationandstatistics.com/AERA2002.pdf
Sutter, C. C., Hulleman, C. S., Givvin, K. B., & Tucker, M. (2002). Utility value trajectories and their relationship with behavioral engagement and performance in introductory statistics. Learning and Individual Differences, 93. https://doi.org/10.1016/j.lindif.2021.102095.
Tempelaar, D., Schim van der Loeff, S., and Gijselaers, W. H. 2007. A structural equation model analyzing the relationship between students’ statistical reasoning abilities, their attitudes toward statistics, and learning approaches. Statistics Education Research Journal, 6(2): 78–102. https://iase-web.org/documents/papers/icots7/2G3_TEMP.pdf
Tremblay, P. F., Gardner, R. C., & Heipel, G. (2000). A model of the relationships among measures of affect, aptitude, and performance in introductory statistics. Canadian Journal of Behavioral Science, 32(1): 40–48. http://dx.doi.org/10.1037/h008709
Watt, H. M. G. (2006). The role of motivation in gendered educational and occupational trajectories related to maths. Educational Research and Evaluation, 12(4), 305-322. http://dx.doi.org.proxy.cc.uic.edu/10.1080/13803610600765562
Wiberg, M. (2009). Teaching statistics in integration with psychology. Journal of Statistics Education, 17(1): 1-16. Retrieved from https://eric.ed.gov/?id=EJ841537
Wigfield, A., and Eccles, J. S. 2000. Expectancy-Value theory of achievement motivation. Contemporary Educational Psychology, 25(1): 68–81. http://doi.org/10.1006/ceps.1999.1015
Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., Tipton, E. et al. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573: 364-372. doi.org/10.1038/s41586-019-1466-y
Yeager, D. S., Romero, C., Paunesku, D., Hulleman, C. S., Schneider, B., Hinojosa, C., Lee, H. Y. et al. (2016). Using design thinking to improve psychological interventions: The case of the growth mindset during the transition to high school. Journal of Educational Psychology, 108(3): 374–391. doi: 10.1037/edu0000098
Yeager, D. S., Walton, G. M., Brady, S. T., Akcinar, E. N., Paunesku, D., Keane, L., Kamentz, D., et al. (2016). Teaching a lay theory before college narrows achievement gaps at scale. Proceedings of the National Academy of Science, 113(24): E3341-E3348. https://doi.org/10.1073/pnas.1524360113
Yumusak, N., Sungur, S., & Cakiroglu, J. (2007). Turkish high school students' biology achievement in relation to academic self-regulation. Educational Research and Evaluation, 13(1), 53-69. http://dx.doi.org.proxy.cc.uic.edu/10.1080/13803610600853749
Zhu, M. The longitudinal effects of teacher judgement and different teacher treatment on students’ academic outcomes. Educational Psychology, 38(5), 648-668. http://dx.doi.org/10.1080/01443410.2017.1412399