Abstract
AbstractExpectancy-value models assume that task values play an important role in engaging successfully with the learning content. However, teacher education students in mathematics only value tasks that deal with university mathematics to a low degree. Offering students profession-related tasks that uncover the connection between university mathematics and school mathematics might help increase students’ value beliefs and effort and decrease costs. Based on Eccles and Wigfield’s (2020) situated expectancy-value theory and using a sample of 133 teacher education students, we investigate whether offering profession-related tasks affects task values, costs, and effort for students with different levels of interest in school mathematics. In a within-subject design, students rated their task values, costs, and effort to solve seven mathematical tasks from number theory, which were either a profession-related task or a regular task with equal demands regarding the intended mathematical activities. Students reported higher utility value for their professional life, higher intrinsic value, and lower costs regarding profession-related tasks. No main effects were found for students’ effort. However, students with a higher interest in school mathematics reported higher effort concerning profession-related tasks than regular tasks. On the theoretical level, our findings contribute to the discussion of the interplay between personal (i.e. interest) and task-specific factors (i.e. relation to future profession) for task values, costs, and effort in university mathematics. One important practical implication of our study is the recommendation to include profession-related tasks in university mathematics courses for teacher education students.
Funder
Deutsche Forschungsgemeinschaft
Otto-von-Guericke-Universität Magdeburg
Publisher
Springer Science and Business Media LLC
Subject
General Mathematics,Education
Reference56 articles.
1. Aeschlimann, B., Herzog, W., & Makarova, E. (2016). How to foster students’ motivation in mathematics and science classes and promote students’ STEM career choice. A study in Swiss high schools. International Journal of Educational Research, 79, 31–41. https://doi.org/10.1016/j.ijer.2016.06.004
2. Barron, K. E., & Hulleman, C. S. (2015). Expectancy-value-cost model of motivation. In J. S. Eccles & K. Salmela-Aro (Eds.), International encyclopedia of social and behavioral sciences: Motivational Psychology (2nd ed., pp. 503–509). Elsevier.
3. Bates, D., Mächler, M., Bolker, B. M., & Walker, S. C. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48.
4. Bauer, T., & Kuennen, E. (2016). Building and measuring mathematical sophistication in pre-service mathematics teachers. Retrieved April 2023 from https://www.mathematik.uni-marburg.de/~tbauer/2016-Building-and-measuring-mathematical-sophistication.pdf
5. Ben-Shachar, M. M. S., Makowski, D., Lüdecke, D., Patil, I., Wiernik, B. M., & Kelley, K. (2023). Package “effectsize”. Retrieved June 2023 from https://cran.r-project.org/web/packages/effectsize/effectsize.pdf