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Promoting cognitive and soft skills acquisition in a disadvantaged public school system: Evidence from the Nurture thru Nature randomized experiment
ARTICLE

, Edward J. Bloustein School of Planning & Public Policy, United States ; , School of Environmental & Biological Sciences, United States ; , Edward J. Bloustein School of Planning & Public Policy, United States

Economics of Education Review Volume 70, Number 1, ISSN 0272-7757 Publisher: Elsevier Ltd

Abstract

It is widely acknowledged that our public schools have failed to produce sufficient levels of high quality STEM education. The mathematics and science performance of minority and disadvantaged students has been especially troubling with blacks and Hispanics substantially underrepresented in the STEM labor market. In this paper we examine the impacts of a STEM enhancement program called Nurture thru Nature (NtN) on the cognitive (academic grades) and soft skills development of 139 elementary school students who attended the program over an eight year period (2010–2017). Utilizing a randomized experimental design or RCT with a control group of 491 elementary school students, we find that NtN slows the deterioration in students’ math and science grades relative to controls and improves soft skills such as conscientiousness, higher order thinking, empathy, and pro-social behavior.

Citation

Jagannathan, R., Camasso, M.J. & Delacalle, M. (2019). Promoting cognitive and soft skills acquisition in a disadvantaged public school system: Evidence from the Nurture thru Nature randomized experiment. Economics of Education Review, 70(1), 173-191. Elsevier Ltd. Retrieved February 26, 2021 from .

This record was imported from Economics of Education Review on June 3, 2019. Economics of Education Review is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.econedurev.2019.04.005

Keywords

References

View References & Citations Map
  1. Aitkin, M., & Longford, N. (1986). Statistical modelling issues in school effectiveness studies. Journal of the Royal Statistical Society Series A (General), 149, pp. 1-43.
  2. Alexander, K.L., Entwisle, D.R., & Olsen, L.S. (2007). Lasting consequences of the summer learning gap. American Sociological Review, 72, pp. 167-180.
  3. Alvarado, S.A., & Muniz, P. (2018). Racial and ethnic heterogeneity in the effect of MESA on AP STEM coursework and college STEM major aspirations. Research in Higher Education, 50, pp. 933-957.
  4. American Institutes for Research (AIR) (2018). What we know about the impact of the 21st CCLC program. Chicago, IL: AIR. Available online: http://www.air.org/page/afterschool-and-expanded-learning.
  5. Attanasio, O.P. (2015). The determinants of human capital formation during the early years of life: Theory, measurement and policies. Journal of the European Economic Association, 13, pp. 949-997.
  6. Attanasio, O. P., Meghir, C., & Nix, E. (2017). Human capital development and parental investment in India. NBER Working Paper 21740. Retrieved at: http://www.nber.org/papers/w21740 Authors (2017) (2018).
  7. Bagiati, A., Yoon, S.Y., Evangelou, D., & Ngambeki, I. (2010). Engineering curricula in early education: Describing the landscape of open resources. Early Childhood Research & Practice, 12(2), pp. 2-15.
  8. Berger, A., Turk-Bicakci, L., & Garett, M. (2013). Early college, early successes: early college high school initiative impact study. Washington, DC: American Institutes for Research.
  9. Borghans, L., Golsteyn, B.H.H., Heckman, J.J., & Humphries, J.E. (2016). What grades and achievement tests measure. Proceedings of the National Academy of Sciences, 113(47), pp. 13354-13359.
  10. Boscia, T. (2013). Serious about STEM. Human Ecology, 41(2), pp. 9-11.
  11. Briggs, D.C., & Peck, F.A. (2015). Using learning progressions to design vertical scales that support coherent inference about student growth. Measurement, 13, p. 75099.
  12. Bryk, A.S., & Raudenbush, S.W. (1987). Application of Hierarchical Linear Models to Assessing change. Psychological Bulletin, 101(1), pp. 147-158.
  13. Business Roundtable (2017). Work-in-progress - How CEO's are helping close america's skills gap. Washington D.C: The Business Roundtable.
  14. Camasso, M.J., & Jagannathan, R. (2017). Experimental evidence for the effectiveness of nature-based learning on academic outcomes in poor urban schools. Journal of Environmental Education, 49, pp. 30-42.
  15. Camasso, M.J., & Jagannathan, R. (2017). The Nurture thru Nature Program: Creating natural science identities in populations of at-risk children. Cambridge Journal of Education, 48, pp. 263-277.
  16. Cameron, A., & Trivedi, P. (2005). Microeconometrics: methods and applications.
  17. Carneiro, P., & Heckman, J.J. (2003). Human capital policy. Inequality in America: what role for human capital policies? Cambridge, MA: MIT Press.
  18. Center for the Economics of Human Development (2015). Conference on measuring and assessing skills report. Conference on measuring and assessing skills report.
  19. Cheslock, J.J., & Rios-Aguilar, C. (2011). Multilevel analysis in higher education research: A multidisciplinary approach. Higher Education: Handbook of Theory and Research, 25.
  20. Clark, P., Crawford, C., Steele, F., & Vignoles, A. (2015). Revisiting fixed- and random effects models: Some considerations for policy-relevant research. Education Economics, 23, pp. 259-277.
  21. Clarke, P. (2012). Education for sustainability: becoming naturally smart. London: Routledge.
  22. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Earlbaum Associates.
  23. Coleman, J.S. (1990). Foundations of social theory. Cambridge, MA: Harvard University Press.
  24. Committee on Equal Opportunities in Science and Engineering. (2015). Report to Congress. http://www.nsf.gov/od/oia/activities/ceose/index.jsp.
  25. Committee on Highly Successful Schools or Programs for K-12 STEM Education (2011). Successful STEM education: a workshop. Washington D.C.: National Academic Press.
  26. Crowder, M.J., & Hand, D.J. (1990). Analysis of repeated measures., p. 257. London: Chapman and Hall.
  27. Cunha, F., Heckman, J.J., & Schennach, S.M. (2010). Estimating the technology of cognitive and noncognitive skill formation. Econometrica, 78(3), pp. 883-931.
  28. Cunha, F., & Heckman, J.J. (2008). Formulating, identifying and estimating the technology of cognitive and noncognitive skill formation. The Journal of Human Resources, 43(4), pp. 738-782.
  29. Curran, P.J., & Hussong, A.M. (2003). The use of latent trajectory models in psychopathology research. Journal of Abnormal Psychology, 112(4), pp. 526-544.
  30. Dewey, J. (1976). The school and society. John Dewey: the middle works, 1, pp. 1-96. Carbondale, IL: Southern Illinois University Press.
  31. Dewey, J. (1990). Between two worlds. John Dewey: the later works, 17, pp. 451-465. Carbondale, IL: Southern Illinois University Press.
  32. Dwyer, J.H. (1983). Statistical models for the social and behavioral sciences. Oxford, England: Oxford University Press.
  33. Fashola, O.S. (1998). Review of extended-day and after-school programs and their effectiveness. Washington, D.C.: Center for Research on the Education of Students Placed at Risk.
  34. FHI-360. (2017). Celebrating the BTE Story: 25 Years, 25 Lessons: Retrieved from http://www.bridge2employment.org/wp-content/uploads/2017/03/25-Celebrating_the_BTE_Story-25Years-25Lessons.pdf.
  35. Fryer, R.G., & Levitt, S.D. (2004). Understanding the black-white test score gap in the first two years of school. The Review of Economics and Statistics, 86, pp. 447-464.
  36. Galloway, T., Lippman, L., Burke, H., Diener, O., & Gates, S. (2017). Measuring soft skills and life skills in international youth development programs: a review and inventory of tools. Washington, DC: USAID's Youth Power Implementation IDIQ.
  37. Garcia, E. (2014). The need to address noncognitive skills in the education policy agenda. Washington, D.C: Economic Policy Institute, Briefing Paper #386. Available online: http://www.epi.org/publication/the-need-to-address-noncognitive-skills-in-the-education-policy-agenda/.
  38. Goldin, C. (2016). Human capital. Handbook of Cliometrics, pp. 55-86. Berlin, Heidelberg: Springer.
  39. Goldstein, H. (1997). Methods in school effectiveness research. School Effectiveness and School Improvement, 8, pp. 369-395.
  40. Goldstein, H. (2010). Multilevel statistical models. London: Wiley.
  41. Gutman, L.M. (2013). The impact of non-cognitive skills on outcomes for young people. Institute of education. London: University of London. Available online: https://educationendowmentfoundation.org.uk/public/files/Publications/EEF_Lit_Review_Non-CognitiveSkills.pdf.
  42. Hanushek, E.A., & Rivkin, S.G. (2009). Harming the best: How schools affect the black-white achievement gap. Journal of Policy Analysis and Management, 28, pp. 366-393.
  43. Heckman, J.J. (2000). Policies to foster human capital. Research in Economics, 54, pp. 3-56.
  44. Heckman, J.J. (2013). Giving kids a fair chance. Cambridge, MA: MIT Press.
  45. Heckman, J.J., & Kautz, T. (2012). Hard evidence on soft skills. Labour Economics, 19(4), pp. 451-464.
  46. Heckman, J.J., & Masterov, D.V. (2007). The productivity argument for investing in young children. Review of Agricultural Economics, 29, pp. 446-493.
  47. Heckman, J.J., Stixrud, J., & Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics, 24(3), pp. 411-482.
  48. Hill, H. (2017). The Coleman Report, 50 years on: What do we know about the role of schools in academic inequality. The ANNALS of the American Academy of Political and Social Science, 674, pp. 9-26.
  49. Hirsch, B.J. (2005). A place to call home: After-school programs for urban youth.
  50. Hollister, R. (2003). The growth in after-school programs and their impact. Washington, DC: Brookings Institution.
  51. Ibarraran, P., Ripani, L., Tabooda, B., Villa, J.M., & Garcia, B. (2014). Life skills, employability and training for disadvantaged youth: Evidence from a randomized evaluation design. IZA Journal of Labor and Development, 3, pp. 1-24.
  52. Iowa Testing Programs (ITP) (2018). Vertical scaling and the assessment of growth. Iowa City, IA: University of Iowa. Available online: http://www.education.uiowa.edu/itp/.
  53. Jagannathan, R., Camasso, M.J., & Delacalle, M. (2018). The effectiveness of the head-heart-hands model for natural and environmental science learning in urban schools. Evaluation and Program Planning, 66, pp. 53-62.
  54. James-Burdumy, S., Dynarski, M., Moor, M., Deke, J., & Mansfield, W. (2005). When schools stay open late: the national evaluation of the 21st century community learning centers program. Available online: http://www.ed.gov.ies.nces.
  55. Jobs For the Future (2017). Bridging to a better future: findings from an evaluation of Bridge-to-College programs for english language learners. Boston, MA: Jobs For the Future.
  56. Judge, T.A., Higgins, C.A., Thoresen, C.J., & Barrick, M.R. (1999). The big five personality traits, general mental ability and career success across the life span. Personnel Psychology, 52, pp. 621-652.
  57. Kemple, J., & Willner, C. (2008). Career Academies: long term impacts on labor market outcomes, educational attainment and transitions to adulthood. New York: MDRC.
  58. Kerlinger, F.N. (1986). Foundations of behavioral research. New York, NY: Holt, Rinehart and Winston.
  59. Klemmer, C.D., Waliczek, T.M., & Zajicek, J.M. (2005). Growing minds: The effect of a school gardening program on the science achievement of elementary students. HortTechnology, 15(3), pp. 448-452.
  60. Kline, P. (1998). The new psychometrics: Science, psychology and measurement. New York, NY: Routledge.
  61. Krishnamurti, A., Ballard, M., & Noam, G.G. (2014). Examining the impact of afterschool STEM programs. Afterschool Alliance Available online: http://afterschoolalliance.org/ExaminingtheImpactof.
  62. Lauer, P.A., Akiba, M., Wilkerson, S.B., Apthorp, H.S., Snow, D., & Martin-Glenn, M.L. (2006). Out-of-school-time programs: A meta-analysis of effects for at-risk students. Review of Educational Research, 76(2), pp. 275-313.
  63. Levine, P.B., & Zimmerman, D.S. (2010). Targeting investments in children: fighting poverty when resources are limited. Chicago: University of Chicago Press.
  64. Lieberman, G.A., & Hoody, L.L. (1998). Closing the achievement gap: using the environment as an integrating context for learning - Results of a national study. Washington D.C: Council of Chief State School Officers. Available online: http://www.seer.org/extras/execsum.pdf.
  65. Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, pp. 1-55.
  66. Lipsey, M.W. (1990). Design sensitivity: Statistical power for experimental research. Thousand Oaks, CA, US: Sage Publications, Inc.
  67. Longford, N.T. (1993). Random coefficient models. Oxford: Clarendon.
  68. Maltese, A.V., & Tai, R.H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among US students. Science Education, 95(5), pp. 877-907.
  69. Manufacturing Institute (2016). Manufacturing jobs over the next decade. Retrieved at: http://www.themanufacturinginstitute.org/Research/Other-Institute-Reports/∼/media/24ECD17A43324F7696BF758A9A116B6F.ashx.
  70. Marsh, H.W., Richards, G.E., & Barnes, J. (1986). Multi-dimensional self-concepts: A long term follow-up of the effect of participation in an Outward Bound Program. Personality and Social Psychology Bulletin, 12, pp. 146-167.
  71. McCombs, J.S., Augustine, C.H., Schwartz, H.L., Bodilly, S.J., McInnis, B., Lichter, D.S., (2011). Making summer count: how summer programs can boost children's learning. Santa Monica, CA: The Wallace Foundation. Published by Rand.
  72. Menard, S.W. (2002). Longitudinal research. Newbury Park, CA: Sage.
  73. National 4-H Council (2016). Grow true leaders: National 4-H Council 2016 Annual Report. Retrieved at: https://4-h.org/wp-content/uploads/2016/03/2016-Annual-Report.pdf.
  74. National Science Foundation (2016). Under-representation of Minorities in Science and Engineering occupations. Retrieved at: https://www.nsf.gov/statistics/2016/nsb20161/#/report/chapter-3/women-and-minorities-in-the-s-e-workforce.
  75. National Science Board (2016). Science and engineering indicators 2016. Arlington, VA: National Science Foundation. Available online: https://www.nsf.gov/statistics/2016/nsb20161/#/.
  76. Oppenheim, A.N. (1992). Questionnaire design, interviewing, and attitude measurement. New York, NY: Printer Publishers.
  77. Ord, J., & Leather, M. (2011). The substance beneath the labels of experiential learning: The importance of John Dewey for outdoor educators. Australian Journal of Outdoor Education, 15(2), pp. 13-23.
  78. Orr, D. W. (1992). Ecological Literacy: Education for a Post Modern World. Albany, NY: State University of New York.
  79. Platt, G. (2008). The hard facts about soft skills measurement. Training Journal, 8, pp. 53-56.
  80. Quay, J., & Seaman, J. (2012). John dewey and education outdoors. Rotterdam: Sense Publishers.
  81. Ramey, C., Bryant, D., Campbell, F., Sparling, J., & Wasik, B. (1988). Early intervention for high-risk children: The Carolina early intervention program. 14 ounces of prevention: a casebook for practioners, pp. 32-43. Washington, DC: American Psychological Association.
  82. Rothwell, J. (2013). The hidden STEM economy. Washington, DC: Brookings Institution Metropolitan Policy Program. Available online: http://www.brookings.edu/research/the-hidden-stem-economy/.
  83. Royal Horticulture Society (2010). Gardening in schools: a vital tool for children's learning. RHS campaign for school gardening. London: Royal Horticulture Society.
  84. Schweinhart, L., Barnes, H., & Weikart, D. (1993). Significant benefits: the high/scope perry pre-school study through age 27. Ypsilanti, MI: Hich Scope Press.
  85. Singer, J.D., & Willett, J.B. (2003). Applied longitudinal data analysis. New York: Oxford University Press.
  86. Singer, J.D. (1998). Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models. Journal of Educational and Behavioral Statistics, 23(4), pp. 323-355.
  87. Smith, L.L., & Matsenbocker, C.E. (2005). Impact of hands-on science through school gardening in Louisiana public elementary schools. HortTechnology, 15, pp. 439-443.
  88. Snijders, T., & Bosker, R. (1999). Multilevel Analysis: an introduction to basic and advanced multilevel modeling. London: Sage.
  89. Soares, F., Babb, S., Diener, O., Gates, S., & Ignatowski, C. (2017). Guiding principles for building soft skills among adolescents and young adults. Washington, DC: USAID's Youth Power Action.
  90. Somers, M., Welbeck, R., Grossman, J.B., & Gooden, S. (2015). An analysis of the effects of an academic summer program for middle school students. NY: MDRC.
  91. Springer, K., & Diffily, D. (2012). The relationship between intensity and breadth of after-school program participation and academic achievement: Evidence from a short-term longitudinal study. Journal of Community Psychology, 40(7), pp. 785-798.
  92. State of New Jersey, Department of Education. (2018). Common core standards. Retrived at http://www.state.nj.us/education/archive/SCA/.
  93. Steinberg, A. (1998). Real learning. New York: Real work.
  94. Stewart, F. (2018). The STEM dilemma: skills that matter to regions. Kalamazoo, MI: W. E. Upjohn Institute.
  95. Tai, R.H. (2012). An examination of the research literature on project lead the way. Indianapolis, IN: Project Lead the Way.
  96. Tai, R.H., Liu, C.Q., Maltese, A.V., & Fan, X. (2006). Planning early for careers in science. Science, New Series, 312(5777), pp. 1143-1144.
  97. Todd, P.E., & Wolpin, K.I. (2003). On the specification and estimation of the production function for cognitive achievement. Economic Journal, 113(485), pp. F3-33.
  98. U. S. Congress Joint Economic Committee Report. (2012). The 2012 Joint Economic Committee Report. https://www.jec.senate.gov/public/index.cfm/2012/12/report-142401bb-daa2-47f3-a550-3dba6c0a0b1e.
  99. U. S. Department of Commerce. (2011). Education supports racial and ethnic equality in STEM” ESA Issue Brief #05‐11, Retrieved at: http://www.esa.doc.gov/sites/default/files/education_supports_racial_and_ethnic_equality_in_stem.pdf.
  100. U. S. Department of Education (2008). A nation accountable: Twenty-five years after A Nation of Risk. Retrieved at: https://www2.ed.gov/rschstat/research/pubs/accountable/accountable.pdf.

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