After-School and Informal STEM Projects: the Effect of Participant Self-Selection
This research represents an unforeseen outcome of the authors’ National Science Foundation Innovation Technology Experiences for Students and Teachers (ITEST) program grant in science education. The grant itself focused on the use of serious educational games (SEGs) in the science classroom, both during and after school, to teach science content and affect student perceptions of science and technology. This study consists of a Bayesian artificial neural network analysis, using the preintervention measures of affect, interest, personality, and cognitive ability, in members of both the treatment and comparison groups to generate the probabilities that students would opt into the treatment group or choose not to participate. It appears, from this sample and the sampling methods of other related studies within the field, that despite sometimes profound results from technology interventions in science, interventions are affecting only those who already have a strong interest in STEM due to the manner in which participants are recruited.
KeywordsEquity Experimental design Serious educational games Informal science
Compliance with Ethical Standards
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
- Adkins, R.C. (2012). American desperately needs more STEM students: here’s how to get them. Forbes 7/05/2012. Retrieved from http://www.forbes.com/sites/forbesleadershipforum/2012/07/09/america-desperately-needsmore-stem-students-heres-how-to-get-them/.
- Byars-Winston, A., Estrada, Y., Howard, C., Davis, D., & Zalapa, J. (2010). Influence of social cognitive and ethnic variables on academic goals of underrepresented students in science and engineering: a multiple-groups analysis. J Couns Psychol, 57(2), 205–218. https://doi.org/10.1037/a0018608.CrossRefGoogle Scholar
- Clark, D. B., Nelson, B. C., Chang, H., Martinez-Garza, M., Slack, K., & D'Angelo, C. M. (2011). Exploring Newtonian mechanics in a conceptually-integrated digital game: comparison of learning and affective outcomes for students in Taiwan and the United States. Comput Educ, 57(3), 2178–2195. https://doi.org/10.1016/j.compedu.2011.05.007.CrossRefGoogle Scholar
- Dimitrov, D. (2009). Quantitative research in education: intermediate and advanced methods. Oceanside: Whittier.Google Scholar
- DiSalvo, B., Guzdail, M., Mcklin, T., Meadows, C., Perry, K., Steward, C., Bruckman, A. (2009). Glitch game testers: African American men breaking open the console. Proceedings of DiGRA.Google Scholar
- Ekstrom, R. B., French, J. W., Harman, H. H., & Dermen, D. (1976). Kit of factor referenced cognitive tests. Princeton: Educational Testing Service.Google Scholar
- Green, M. & Ohlsson, M. (2007). Comparison of standard resampling methods for performance estimation of artificial neural networks. Third Annual Conference on Computational Intelligence in Medicine and Healthcare.Google Scholar
- Jones, L.K. (1987). The Career Key. Raleigh, NC: Author. (Originally published by Ferguson in Chicago).Google Scholar
- Ketelhut, D. J. (2010). Assessing gaming, computer and scientific inquiry self-efficacy in a virtual environment. In L. A. Annetta & S. Bronack (Eds.), Serious educational game assessment: practical methods and models for educational games, simulations, and virtual worlds (pp. 1–18). Amsterdam: Sense Publishers.Google Scholar
- Lamb, R., Annetta, L.A., Meldrum, J., & Vallett, D. (2012). Constructing and validating the science interest survey. International Journal of Science and Mathematics Education, 10(3), 643–688.Google Scholar
- Lamb, R. L., Vallett, D.B., & Annetta, L. (2014). Development of a short form measure or science and technology self-efficacy using Rasch analysis. Journal of Science Education and Technology, 23(5), 641–657. 10.1007/s10956-014-9491-y.
- Lamb, R., Cavegnetto, A., & Akmal, T. (2016). Examination of the nonlinear dynamic systems associated with student cognition while engaging in science information processing. International Journal of Science and Mathematics Education, 14(Suppl 1), S187–S205.Google Scholar
- Lee, J., Liu, X., Amo, L. C., & Wang, W. L. (2013). Multilevel linkages between state standards, teacher standards, and student achievement: testing external versus internal standards-based education models. Educational Policy, 0895904813475708.Google Scholar
- National Science Foundation (2010). Science and engineering indicators 2010. Arlington, VA.Google Scholar
- Palmer, R. T., Maramba, D. C., Elon Dancy, I. I., & T. (2011). A qualitative investigation of factors promoting the retention and persistence of students of color in STEM. Journal Of Negro Education, 80(4), 491–504.Google Scholar
- Salto, L. M., Riggs, M. L., Delgado De Leon, D., Casiano, C. A., & De Leon, M. (2014). Underrepresented minority high school and college students report STEM-pipeline sustaining gains after participating in the Loma Linda University Summer Health Disparities Research Program. PLoS One, 9(9), 1–13. https://doi.org/10.1371/journal.pone.0108497.CrossRefGoogle Scholar
- Schifter, C. C., Ketelhut, D., & Nelson, B. C. (2012). Presence and middle school students’ participation in a virtual game environment to assess science inquiry. Journal of Educational Technology & Society, 15(1), 53–63.Google Scholar
- Schukajlow, S., Leiss, D., Pekrun, R., Blum, W., Müller, M., & Messner, R. (2012). Teaching methods for modeling problems and students’ task-specific enjoyment, value, interest and self-efficacy expectations. Educ Stud Math, 79(2), 215–237. https://doi.org/10.1007/s10649-011-9341-2.CrossRefGoogle Scholar
- Simpson, J. C. (2000). Segregated by subject: racial differences in the factors influencing academic major between European Americans, Asian Americans, and African, Hispanic, and Native Americans. J High Educ, 72, 63–100.Google Scholar
- Slovacek, S. P., Peterfreund, A. R., Glenn, D. K., Whittinghill, J. C., Tucker, S., Rath, K. A., & Reinke, Y. G. (2011). Minority students severely underrepresented in science, technology engineering and math. Journal Of STEM Education: Innovations & Research, 12(1/2), 5–16.Google Scholar
- Zarrett, N., Malanchuk, O., Davis-Kean, P. E., & Eccles, J. (2006). Examining the gender gap in IT by race: young adults’ decisions to pursue an IT career. In J. M. C. W. Aspray (Ed.), Women and information technology: research on underrepresentation (pp. 55–58). Cambridge: MIT Press. https://doi.org/10.7551/mitpress/9780262033459.003.0002.CrossRefGoogle Scholar