Examining Relationships among Choice, Affect, and Engagement in Summer STEM Programs
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Out-of-school time programs focused on science, technology, engineering and mathematics (STEM) have proliferated recently because they are seen as having potential to appeal to youth and enhance STEM interest. Although such programs are not mandatory, youth are not always involved in making the choice about their participation and it is unclear whether youth’s involvement in the choice to attend impacts their program experiences. Using data collected from experience sampling, traditional surveys, and video recordings, we explore relationships among youth’s choice to attend out-of-school time programs (measured through a pre-survey) and their experience of affect (i.e., youth experience sampling ratings of happiness and excitement) and engagement (i.e., youth experience sampling ratings of concentration and effort) during program activities. Data were collected from a racially and ethnically diverse sample of 10–16 year old youth (n = 203; 50% female) enrolled in nine different summer STEM programs targeting underserved youth. Multilevel analysis indicated that choice and affect are independently and positively associated with momentary engagement. Though choice to enroll was a significant predictor of momentary engagement, positive affective experiences during the program may compensate for any decrements to engagement associated with lack of choice. Together, these findings have implications for researchers, parents, and educators and administrators of out-of-school time programming.
KeywordsChoice Affect Engagement Out-of-school time STEM education
P.B. developed the research question, analytic plan, was involved in the interpretation of data, wrote and revised the manuscript. J.R. critically revised the manuscript, aided in analysis, and interpreted data. J.S. co-conceived of the original study design, critically revised the manuscript, and interpreted data. N.N. co-conceived of the original study design, and aided in data collection. All authors read and approved of the final manuscript.
This material is based upon work supported by the National Science Foundation Grant DRL-1421198. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not reflect the views of the National Science Foundation.
Data Sharing Declaration
This manuscript’s data will not be deposited.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- Allen, J. P., Hauser, S. T., Bell, K. L., & O’Connor, T. G. (1994). Longitudinal assessment of autonomy and relatedness in adolescent-family interactions as predictors of adolescent ego development and self-esteem. Child Development, 65(1), 179–194. https://doi.org/10.1111/j.1467-8624.1994.tb00743.x.PubMedGoogle Scholar
- Bronfenbrenner, U., & Morris, P. A. (1998). The ecology of developmental processes. In R. M. Lerner (Ed.), Handbook of child psychology (5th ed.). (pp. 993–1028). New York: Wiley.Google Scholar
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: L. Erlbaum Associates.Google Scholar
- Dabney, K. P., Tai, R. H., Almarode, J. T., Miller-Friedmann, J. L., Sonnert, G., Sadler, P. M., & Hazari, Z. (2012). Out-of-school time science activities and their association with career interest in stem. International Journal of Science Education, 2(1), 63–79. https://doi.org/10.1080/21548455.2011.629455.Google Scholar
- Dijksterhuis, A. (2010). Automaticity and the unconscious. In S. T. Fiske, D. T. Gilbert & G. Lindzey (Eds.), Handbook of social psychology 1, (228–267). Hoboken, NJ: Wiley.Google Scholar
- Efklides, A., & Petkaki, C. (2005). Effects of mood on students’ metacognitive experiences. Learning and Instruction, 15(5), 415–431. https://doi.org/10.1016/j.learninstruc.2005.07.010.Google Scholar
- Elam, M. E., Donham, B. L., & Solomon, S. R. (2012). An engineering summer program for underrepresented students from rural school districts. Journal of STEM Education, 13(2), 35–44.Google Scholar
- Fayer, S., Lacey, A., & Watson, A. (2017). STEM occupations: past, present, and future. Retrieved from Bureau of Labor Statistics Website: https://www.bls.gov/spotlight/2017/science-technology-engineering-and-mathematics-stem-occupations-past-present-and-future/pdf/science-technology-engineering-and-mathematics-stem-occupations-past-present-and-future.pdf.
- Frank, K. A., Maroulis, S. J., Duong, M. Q., & Kelcey, B. M. (2013). What would it take to change an inference? Using Rubin’s causal model to interpret the robustness of causal inferences. Educational Evaluation and Policy Analysis, 35(4), 437–460. https://doi.org/10.3102/0162373713493129.Google Scholar
- Gogol, K., Brunner, M., Goetz, T., Martin, R., Ugen, S., Keller, U., & Preckel, F. (2014). “My questionnaire is too long!” The assessments of motivational-affective constructs with three-item and single-item measures. Contemporary Educational Psychology, 39(3), 188–205. https://doi.org/10.1016/j.cedpsych.2014.04.002.Google Scholar
- Linnenbrink-Garcia, L, Wormington, S. V. & Ranellucci, J. (2016). Measuring affect in educational contexts: A Circumplex Approach. In M. Zembylas, P. A. Schutz (Eds.) Methodological Advances in Research on Emotion and Education. New York, NY: Springer US. .Google Scholar
- Mohr-Schroeder, M. J., Jackson, C., Miller, M., Walcott, B., Little, D. L., Speler, L., Schooler, W., & Schroeder, D. C. (2014). Developing middle school students’ interests in stem via summer learning experiences: See blue stem camp. School Science and Mathematics, 114(6), 291–301. https://doi.org/10.1111/ssm.12079.Google Scholar
- Naftzger, N., Moroney, D., Schmidt, J., & Shumow, L. (2014). STEM interest and engagement study. National Science Foundation Grant No: DRL-1421198.Google Scholar
- National Academies of Sciences, Engineering, and Medicine. (2017). Building America’s Skilled Technical Workforce. Washington, DC: The National Academies Press. https://doi.org/10.17226/23472.Google Scholar
- Patall, E. A., Steingut, R. R., Vasquez, A. C., Trimble, S. S., Pituch, K. A., & Freeman, J. L. (2017). Daily autonomy supporting or thwarting and students’ motivation and engagement in the high school science classroom. Journal of Educational Psychology. https://doi.org/10.1037/edu0000214.
- Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications.Google Scholar
- Renninger, K. A. (2007). Interest and motivation in informal science learning. IEEE Computer Society Press. http://www.informalscience.com/researches/Renninger_Commissioned_Paper. pdf.Google Scholar
- Rosenberg, J. R., Xu, R., & Frank, K. A. (2018). konfound: Sensitivity analysis based on Rubin’s causal model. https://jrosen48.github.io/konfound/.
- Vandell, D. L., Warschauer, M., O’Cadiz, M. P., & Hall, V. (2008). Two year evaluation study of the Tiger Woods Learning Center: Volume I, II, III. Irvine, CA: Tiger Woods Foundation.Google Scholar
- Zimmer-Gembeck, M. J., & Collins, W. A. (2003). Autonomy development during adolescence. In G. R. Adams & M. D. Berzonsky (Eds.), Blackwell handbooks of developmental psychology (pp. 175–204). Malden, MA: Blackwell Publishing.Google Scholar