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Alignment of educational aspirations and career plans in high school with Mobile app technology

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Abstract

Init2Winit is a gamified mobile application designed to promote college and career knowledge among adolescents. Init2Winit offers students multiple opportunities to explore college and career pathways using game tunnels; this tunnel play informs students’ understanding of how mis/aligned choices can have varying consequences for their future. Our study examines player performance in the career tunnel--where students attempt to align their educational expectations with chosen career pathways. Students earn points when their educational expectations are correctly aligned with their desired careers and expected salaries. Init2Winit was tested in Midwest urban and rural high schools using a sample of 186 high students. Results show that students who earned high alignment scores increased their college-going expectations. Subsequent game plays increased students’ alignment between college choices, career plans, and realistic salary projections.

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References

  • Allensworth, E., Nomi, T., Montgomery, N., & Lee, V. E. (2009). College preparatory curriculum for all: Academic consequences of requiring algebra and English I for ninth graders in Chicago. Educational Evaluation and Policy Analysis, 31(4), 367–391.

    Article  Google Scholar 

  • Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2020). Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Education and Information Technologies, 25(1), 419–435.

    Article  Google Scholar 

  • Anderson, M., & Jiang, J. (2018). Teens, Social Media & Technology. Washington, D.C.: Pew Research Center Retrieved from https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/.

    Google Scholar 

  • Anonymous. (1999). The ambitious generation: America’s teenagers, Motivated but Directionless. Yale University Press.

  • Anonymous. (2014). The college ambition program: A realistic transition strategy for traditionally disadvantaged students. Educational Researcher, 44(7), 394–403.

    Google Scholar 

  • Anonymous. (2017). Co-development of education aspirations and postsecondary enrollment especially among students who are low income and minority. Research in Human Development, 14(2), 143–160.

    Article  Google Scholar 

  • Anonymous. (2019a). The effects of alignment of educational expectations and occupational aspirations on labor market outcomes: Evidence from NLSY79. The Journal of Higher Education, 90(6), 992–1015. https://doi.org/10.1080/00221546.2019.1615333.

    Article  Google Scholar 

  • Anonymous. (2019b). Advancing workforce readiness among low-income and minority high school students. Workforce Readiness and the Future of Work, 53.

  • Aud, S., Hussar, W., Johnson, F., Kena, G., Roth, E., Manning, E., ..., Zhang, J. (2012). The condition of education 2012. NCES 2012–045. National Center for Education Statistics.

  • Avery, C., & Kane, T. J. (2004). Student perceptions of college opportunities. The Boston COACH program. In College choices: The economics of where to go, when to go, and how to pay for it (pp. 355-394). University of Chicago Press. Retrieved from: https://www.nber.org/chapters/c10104.pdf. Accessed 21 Sep 2004.

  • Bettinger, E. P., & Baker, R. B. (2014). The effects of student coaching: An evaluation of a randomized experiment in student advising. Educational Evaluation and Policy Analysis, 36(1), 3–19. https://doi.org/10.3102/0162373713500523.

    Article  Google Scholar 

  • Bettinger, E., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2012). The role of application assistance and information in college decisions: Results from the H&R Block FAFSA experiment. The Quarterly Journal of Economics, 127(3), 1205–1242. https://doi.org/10.1093/qje/qjs017.

    Article  Google Scholar 

  • Campbell, T., & Wescott, J. (2019). Profile of undergraduate students: Attendance, distance and remedial education, degree program and field of study, demographics, financial aid, financial literacy, employment, and military status: 2015–16. Washington, D.C.: U.S. Department of Education Retrieved from https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2019467.

    Google Scholar 

  • Carnevale, A, P., Smith, N., Strohl, J. (2013). Recovery: Job growth and education requirements through 2020. Georgetown University Center on Education and the Workforce.

  • Castleman, B. L., & Page, L. C. (2015). Summer nudging: Can personalized text messages and peer mentor outreach increase college going among low-income high school graduates? Journal of Economic Behavior & Organization, 115, 144–160. https://doi.org/10.1016/j.jebo.2014.12.008

  • Cataldi, E, F., Bennett, C, T., Chen, X. (2018). First-generation students: College access, persistence, and Postbachelor's outcomes. Stats in brief. NCES 2018-421. National Center for education statistics.

  • Cohodes, S. R., & Goodman, J. S. (2014). Merit aid, college quality, and college completion: Massachusetts’ Adams scholarship as an in-kind subsidy. American Economic Journal: Applied Economics, 6(4), 251–285.

    Google Scholar 

  • Deil-Amen, R., & DeLuca, S. (2010). The underserved third: How our educational structures populate an educational underclass. Journal of Education for Students Placed at Risk, 15(1–2), 27–50.

    Article  Google Scholar 

  • Deming, D., Dynarski, S. (2010). College aid. In Targeting investments in children: Fighting poverty when resources are limited (pp. 283-302). University of Chicago Press.

  • DesJardins, S. L., & McCall, B. P. (2014). The impact of the gates millennium scholars program on college and post-college related choices of high ability, low-income minority students. Economics of Education Review, 38, 124–138.

    Article  Google Scholar 

  • Deterding, S., Sicart, M., Nacke, L., O’Hara, K., Dixon, D. (May, 2011). Gamification: Using game-design elements in non-gaming contexts. Paper presented at the CHI 2011 Workshop, Vancouver, BC. Abstract retrieved from http://gamification-research.org/wp-content/uploads/2011/04/01-Deterding-Sicart-Nacke-OHara-Dixon.pdf

  • Dillon, E. W., & Smith, J. A. (2017). Determinants of the match between student ability and college quality. Journal of Labor Economics, 35(1), 45–66. https://doi.org/10.1086/687523.

    Article  Google Scholar 

  • Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170–179.

    Article  Google Scholar 

  • Herodotou, C. (2018). Young children and tablets: A systematic review of effects on learning and development. Journal of Computer Assisted Learning, 34(1), 1–9.

    Article  Google Scholar 

  • Horn, L. J., Chen, X., & Chapman, C. (2003). Getting ready to pay for college: What students and their parents know about the cost of college tuition and what they are doing to find out. Washington, DC: National Center for Education Statistics.

  • Houle, J. N., & Addo, F. R. (2019). Racial disparities in student debt and the reproduction of the fragile black middle class. Sociology of Race and Ethnicity, 5(4), 562–577.

    Article  Google Scholar 

  • Hoxby, C., & Avery, C. (2013). The missing “one-offs”: The hidden supply of high-achieving, low-income students. Brookings Papers on Economic Activity, 44(1), 1–65.

    Article  Google Scholar 

  • Jacob, B. A., & Linkow, T. W. (2011). Educational expectations and attainment. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 133–162). New York: Russell Sage Foundation.

  • Jury, M., Smeding, A., Stephens, N. M., Nelson, J. E., Aelenei, C., & Darnon, C. (2017). The experience of low-SES students in higher education: Psychological barriers to success and interventions to reduce social-class inequality. Journal of Social Issues, 73(1), 23–41.

    Article  Google Scholar 

  • Klímová, B. (2017). Mobile phones and/or smartphones and their apps for teaching English as a foreign language. Education and Information Technologies, 23(3), 1091–1099.

  • Klimova, B., & Kacet, J. (2017). Efficacy of computer games on language learning. Turkish Online Journal of Educational Technology-TOJET, 16(4), 19–26.

    Google Scholar 

  • Kramer, J. W. (2020). Experimental evidence on the effects (or lack thereof) of informational framing during the college transition. AERA Open, 6(1), 2332858420908536.

    Article  Google Scholar 

  • Kumar, B. A., & Chand, S. S. (2019). Mobile learning adoption: A systematic review. Education and Information Technologies, 24(1), 471–487.

    Article  Google Scholar 

  • Lowry, K. (2017). Community college choice and the role of Undermatching in the lives of African Americans. Community College Journal of Research and Practice, 41(1), 18–26.

    Article  Google Scholar 

  • Luna-Nevarez, C., & McGovern, E. (2018). On the use of mobile apps in education: The impact of digital magazines on student learning. Journal of Educational Technology Systems, 47(1), 17–31.

    Article  Google Scholar 

  • McFarland, J., Hussar, B., Zhang, J., Wang, X., Wang, K., Hein, S., Diliberti, M., Forrest Cataldi, E., Bullock Mann, F., and Barmer, A. (2019). The condition of education 2019 (NCES 2019-144). U.S. Department of Education. Washington, DC: National Center for education statistics. Retrieved [date] from https://nces.ed.gov/pubsearch/pubsinfo.Asp?Pubid=2019144

  • Michigan Department of Education. (2019). School Characteristics. Retrieved from https://www.mischooldata.org/Default3.aspx

  • Mouza, C., & Barrett-Greenly, T. (2015). Bridging the app gap: An examination of a professional development initiative on Mobile learning in urban schools. Computers & Education., 88, 1–14. https://doi.org/10.1016/j.compedu.2015.04.009.

    Article  Google Scholar 

  • National Center for O*NET Development n.d. Job zone. O*NET OnLine. Retrieved from https://www.onetonline.org/find/zone Accessed 06 Nov 2018

  • Oreopoulos, P., Petronijevic, U. (2013). Making college worth it: A review of research on the returns to higher education (no. w19053). National Bureau of Economic Research.

  • Oymak, C. (2018). High school Students’ views on who influences their thinking about education and careers. Stats in brief. NCES 2018-088. National Center for education statistics.

  • Page, L. C., & Gehlbach, H. (2017). How an artificially intelligent virtual assistant helps students navigate the road to college. AERA Open, 3(4), 2332858417749220.

    Article  Google Scholar 

  • Perrin, A. (2017). Smartphones help blacks, Hispanics bridge some–but not all–digital gaps with whites. Retrieved from Pew Research Center website: https://pewresearch-org-preprod.go-vip.co/fact-tank/2019/08/20/smartphones-help-blacks-hispanics-bridge-some-but-not-all-digital-gaps-with-whites/

  • Perry, B., Martinez, E., Morris, E., Link, T., & Leukefeld, C. (2016). Misalignment of career and educational aspirations in middle school: Differences across race, ethnicity, and socioeconomic status. Social Sciences, 5(3), 35.

    Article  Google Scholar 

  • Pew Research Center. (2017). Mobile fact sheet. Washington, D.C: Retrieved from http://www.pewinternet.org/fact-sheets/mobile

  • Pordelan, N., Sadeghi, A., Abedi, M. R., & Kaedi, M. (2020). Promoting student career decision-making self-efficacy: An online intervention. Education and Information Technologies, 25(2), 985–996.

    Article  Google Scholar 

  • Renbarger, R., & Long, K. (2019). Interventions for postsecondary success for low-income and high-potential students: A systematic review. Journal of Advanced Academics, 30(2), 178–202.

    Article  Google Scholar 

  • Rios-Aguilar, C., Kiyama, J. M., Gravitt, M., & Moll, L. C. (2011). Funds of knowledge for the poor and forms of capital for the rich? A capital approach to examining funds of knowledge. Theory and Research in Education, 9(2), 163–184.

    Article  Google Scholar 

  • Robb, C. A. (2017). College student financial stress: Are the kids alright? Journal of Family and Economic Issues, 38(4), 514–527.

    Article  Google Scholar 

  • Robb, C. A., Moody, B., & Abdel-Ghany, M. (2012). College student persistence to degree: The burden of debt. Journal of College Student Retention: Research, Theory & Practice, 13(4), 431–456.

    Article  Google Scholar 

  • Rowan-Kenyon, H. T., Perna, L. W., & Swan, A. K. (2011). Structuring opportunity: The role of school context in shaping high school students’ occupational aspirations. The Career Development Quarterly, 59(4), 330–344.

    Article  Google Scholar 

  • Sabates, R., Harris, A. L., & Staff, J. (2011). Ambition gone awry: The long-term socioeconomic consequences of misaligned and uncertain ambitions in adolescence. Social Science Quarterly, 92(4), 959–977.

    Google Scholar 

  • Sánchez, J., & Olivares, R. (2011). Problem solving and collaboration using mobile serious games. Computers & Education, 57(3), 1943–1952.

    Article  Google Scholar 

  • Schmitt-Wilson, S., & Faas, C. (2016). Alignment of educational and occupational expectations influences on young adult educational attainment, income, and underemployment. Social Science Quarterly, 97(5), 1174–1188. https://doi.org/10.1111/ssqu.12244.

    Article  Google Scholar 

  • Schudde, L., & Brown, R. S. (2019). Understanding variation in estimates of diversionary effects of community college entrance: A systematic review and meta-analysis. Sociology of Education, 92(3), 247–268. https://doi.org/10.1177/0038040719848445.

    Article  Google Scholar 

  • Singh, G. (2018). 29 app store stats 2018–19 that proves Apple’s uprising growth trend. Retrieved from appinventiv website: https://appinventiv.com/blog/apple-app-store-statistics/

  • Smith, J., Pender, M., & Howell, J. (2013). The full extent of student–college academic undermatch. Economics of Education Review, 32, 247–261.

    Article  Google Scholar 

  • Snyder, T, D., de Brey, C., Dillow, S, A. (2016). Digest of education statistics 2014, NCES 2016–006. National Center for Education Statistics.

  • West, M., & Vosloo, S. E. (2013). UNESCO policy guidelines for mobile learning. Paris: UNESCO.

    Google Scholar 

  • Xiangming, L., & Song, S. (2018). Mobile technology affordance and its social implications: A case of “Rain classroom”: Mobile technology affordance & social implications. British Journal of Educational Technology, 49(2), 276–291.

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Acknowledgements

The authors want to thank Lindsey Young and Amieris Lavender for their assistance with an earlier data collection that contributed to this study. This work has been supported by the National Science Foundation under Grant No. 1316702 and No. 1661236. The opinions expressed do not necessarily reflect the views of the Foundation.

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Correspondence to I. Chien Chen.

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Chen, I.C., Rocha-Beverly, C. & Schneider, B. Alignment of educational aspirations and career plans in high school with Mobile app technology. Educ Inf Technol 26, 1091–1109 (2021). https://doi.org/10.1007/s10639-020-10296-z

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