The emotional costs of computers: an expectancy-value theory analysis of predominantly low-socioeconomic status minority students’ STEM attitudes

References

• Alabama Department of Education. (2015). Alabama Department of Education Data Center. from http://www.alsde.edu/dept/data/Pages/home.aspx.
   Google Scholar
• Allen-Ramdial, S.-A. A., & Campbell, A. G. (2014). Reimagining the pipeline: Advancing STEM diversity, persistence, and success. BioScience, 64(7), 612–618. doi: 10.1093/biosci/biu076
   PubMed Web of Science ®Google Scholar
• Anderson, L., & Ward, T. (2013). Expectancy-value models for the STEM persistence plans of ninth-grade, high-ability students: A comparison between black, Hispanic, and white students. Science Education, 98(2), 216–242. doi: 10.1002/sce.21092
   Web of Science ®Google Scholar
• Atkinson, J. (1957). Motivational determinants of risk-taking behavior. Psychology Review, 64(6), 359–372. doi: 10.1037/h0043445
   Google Scholar
• Ball, C., Huang, K.-T., Cotten, S. R., Rikard, R. V., & Coleman, L. O. (2016). Invaluable values: An expectancy-value theory analysis of youths’ academic motivations and intentions. Information, Communication & Society, 1–21. doi: 10.1080/1369118X.2016.1139616
   Web of Science ®Google Scholar
• Barron, K., & Hulleman, C. (2014). Expectancy-value-cost model of motivation. In J. Eccles & K. Salmelo-Aro (Eds.), International encyclopedia of social and behavioral sciences (2nd ed.: Motivational Psychology, pp. 503–509). Oxford: Elsvier.
   Google Scholar
• Battle, A., & Wigfield, A. (2003). College women’s value orientations toward family, career, and graduate school. Journal of Vocational Behavior, 62, 56–75. doi: 10.1016/S0001-8791(02)00037-4
   Web of Science ®Google Scholar
• Beede, D., Julian, T., Khan, B., Lehrman, R., McKittrick, G., Langdon, D., & Doms, M. (2011). Education supports racial and ethnic equality in STEM. Washington, DC: Economics and Statistics Administration.
   Google Scholar
• Briggs, C. (2016). The policy of STEM diversity: Diversifying STEM programs in higher education. Journal of STEM Education: Innovations and Research, 17(4), 5–7.
   Google Scholar
• Carnes, M., Schuler, L., Sarto, G. E., Lent, S. J., & Bakken, L. (2006). Increasing sex and ethnic/racial diversity of researchers in aging: Some promising strategies at the postdoctoral level. Journal of the American Geriatrics Society, 54(6), 980–985. doi: 10.1111/j.1532-5415.2006.00739.x
   PubMed Web of Science ®Google Scholar
• Carnevale, A., Smith, N., & Melton, M. (2014). STEM. Washington, DC: Center on Education and the Workforce.
   Google Scholar
• Carter, A., Cotten, S., Gibson, P., O’Neal, L., Simoni, Z., Stringer, K., & Watkins, L. (2014). Integrating computing across the curriculum: Incorporating technology into STEM education. In Z. Yang, H. Hao Yang, D. Wu, & S. Liu (Eds.), Transforming K-12 classrooms with digital technology (pp. 165–192). Hershey, PA: IGI Global.
   Google Scholar
• Chen, X., & Weko, T. (2009). Students who study science, technology, engineering, and mathetatics (STEM) in postsecondary education. Washington, DC: U.S. Department of Education.
   Google Scholar
• Chiang, E., Byrd, S., & Molin, A. (2011). Children’s percieved cost for exercise: Application of an expectancy-value paradigm. Health Education & Behavior, 38(2), 143–149. doi: 10.1177/1090198110376350
   PubMed Web of Science ®Google Scholar
• Coltman, T., Devinney, T. M., Midgley, D. F., & Venaik, S. (2008). Formative versus reflective measurement models: Two applications of formative measurement. Journal of Business Research, 61(12), 1250–1262. doi: 10.1016/j.jbusres.2008.01.013
   Web of Science ®Google Scholar
• Committee on STEM Education. (2013). Federal STEM education 5-year plan. Washington, DC: National Science and Technology Council.
   Google Scholar
• Conley, A. (2011). Patterns of motivation beliefs: Combining achievement goal and expectancy-value perspectives. Journal of Educational Psychology, 104(1), 32–47. doi: 10.1037/a0026042
   Web of Science ®Google Scholar
• Cotten, S. (2010). XO laptops, education, and connection: Results from the Birmingham youth and technology study. Paper presented at the Texas A&M Race and Ethnic Studies Institute, College Station, Texas.
   Google Scholar
• Cover, B., Jones, J. I., & Watson, A. (2011). Science, technology, engineering, and mathematics (STEM) occupations: A visual essay. Monthly Labor Review, 134(5), 3–15.
   Web of Science ®Google Scholar
• DiMaggio, P., Hargittai, E., Celeste, C., & Shafer, S. (2004). From unequal access to differentiated use: A literature review and agenda for research on digital inequality. In K. Neckerman & S. Shafer (Eds.), Social inequality (pp. 355–400). New York, NY: Russell Sage Foundation.
   Google Scholar
• Eccles, J. (2005). Subjective task value and the Eccles et al. Model of achievement-related choices. In A. Elliot & C. Dweck (Eds.), Handbook of competence and motivation (pp. 105–121). New York, NY: The Guildford Press.
   Google Scholar
• Eccles, J., Adler, T., Futterman, R., Goff, S., Kaczala, C., Meece, J., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). San Francisco, CA: W. H. Freeman.
   Google Scholar
• Eccles, J., Wigfield, A., Harold, R., & Blumenfeld, P. (1993). Age and gender differences in children’s self- and task perceptions during elementary school. Child Development, 64(3), 830–847. doi: 10.2307/1131221
   PubMed Web of Science ®Google Scholar
• Eglash, R., Babbitt, W., Bennett, A., Bennett, K., Callahan, B., Davis, J., & Krishnamoorthy, M. (2017). Culturally situated design tools: Generative justice as a foundation for STEM diversity (pp. 132–151). Hershey, PA: IGI Global.
   Google Scholar
• Ellwart, T., & Konradt, U. (2011). Formative versus reflective measurement: An illustration using work–family balance. The Journal of Psychology, 145(5), 391–417. doi: 10.1080/00223980.2011.580388
   PubMed Web of Science ®Google Scholar
• Flake, J., Barron, K., Hulleman, C., McCoach, B., & Welsh, M. (2015). Measuring cost: The forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41, 232–244. doi: 10.1016/j.cedpsych.2015.03.002
   Web of Science ®Google Scholar
• Frehill, L., Di Fabio, N., & Hill, S. (2008). Confronting the “new” American dilemma-underrepresented minorities in engineering: A data-based look at diversity. White Plains, NY: National Action Council for Minorities in Engineering.
   Google Scholar
• Gaspard, H., Dicke, A.-L., Flunger, B., Brisson, B. M., Häfner, I., Nagengast, B., & Trautwein, U. (2015). Fostering adolescents’ value beliefs for mathematics with a relevance intervention in the classroom. Developmental Psychology, 51(9), 1226–1240. doi: 10.1037/dev0000028
   PubMed Web of Science ®Google Scholar
• Gaspard, H., Dicke, A.-L., Flunger, B., Schreier, B., Häfner, I., Trautwein, U., & Nagengast, B. (2015). More value through greater differentiation: Gender differences in value beliefs about math. Journal of Educational Psychology, 107(3), 663–677. doi: 10.1037/edu0000003
   Web of Science ®Google Scholar
• Gilbert, M. (2010). Theorizing digital and urban inequalities: Critical geographies of ‘race’, gender and technological capital. Information, Communication & Society, 13(7), 1000–1018. doi: 10.1080/1369118X.2010.499954
   Web of Science ®Google Scholar
• Hargittai, E., & Hinnant, A. (2008). Digital inequality: Differences in young adults’ use of the internet. Communication Research, 35(5), 602–621. doi: 10.1177/0093650208321782
   Web of Science ®Google Scholar
• Hicks, M. (2017). Programmed inequality: How Britain discarded women technologists and lost its edge in computing. Cambridge, MA: The MIT Press.
   Google Scholar
• Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
   Google Scholar
• Huang, K.-T., Cotten, S. R., & Rikard, R. V. (2017). Access is not enough: The impact of emotional costs and self-efficacy on the changes in African-American students’ ICT use patterns. Information, Communication & Society, 20(4), 637–650. doi: 10.1080/1369118X.2016.1203456
   Web of Science ®Google Scholar
• Huang, K.-T., Robinson, L., & Cotten, S. (2015). Mind the emotional gap: The impact of emotional costs on student learning outcomes. In L. Robinson, S. Cotten, & J. Schulz, T. M. Hale, & A. Williams (Eds.), Communication and information technologies annual (studies in media and communications, (vol. 10, pp. 121–144). Bingley, West Yorkshire: Emerald Group.
   Google Scholar
• Matusovich, H., Streveler, R., Loshbaugh, H., Miller, R., & Olds, B. (2008). Will I succeed in engineering? Using expectancy-value theory in a longitudinal investigation of students’ beliefs. Paper presented at the American Society for Engineering Education 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania.
   Google Scholar
• McIntosh, D. H. (2016). A bridge to the stars: An innovative pipeline to improve STEM diversity. Paper presented at the American Astronomical Society Meeting Abstracts.
   Google Scholar
• Melguizo, T., & Wolniak, G. (2012). The earnings benefits of majoring in STEM fields among high achieving minority students. Research in Higher Education, 53(4), 383–405. doi: 10.1007/s11162-011-9238-z
   Web of Science ®Google Scholar
• Milfort, M. (2012). An examination of the information technology job market credentials that work. Washington, DC: Jobs for the Future.
   Google Scholar
• National Center for Education Statistics. (2015a). Digest of educaiton statistics. Retrieved from https://nces.ed.gov/programs/digest/d14/tables/dt14_219.70.asp?current=yes/; https://nces.ed.gov/programs/digest/d14/tables/dt14_322.40.asp?current=yes
   Google Scholar
• National Center for Education Statistics. (2015b). Selected findings from PISA 2015. Retrieved June 27, 2017, from https://nces.ed.gov/surveys/pisa/pisa2015/pisa2015highlights_1.asp
   Google Scholar
• National Center for Science and Engineering Statistics. (2017). National science foundation women, minorities, and persons with disabilities in science and engineering: 2017. Retrieved June 27, 2017, from https://www.nsf.gov/news/news_images.jsp?cntn_id=190946&org=NSF
   Google Scholar
• National Math and Science Initiative. (2015). STEM education statistics. Retrieved from https://www.nms.org/AboutNMSI/TheSTEMCrisis/STEMEducationStatistics.aspx
   Google Scholar
• National Science Board. (2016). Science and engineering indicators 2016. Retrieved from https://www.nsf.gov/statistics/2016/nsb20161/#/digest/u-s-and-global-stem-education
   Google Scholar
• Obama, B. (2015). Investing in America’s future: Preparing students with STEM skills. Washington, DC: White House Office of Science and Technology Policy.
   Google Scholar
• Oldenziel, R. (1999). Making technology masculine: Men, women, and modern machines in America, 1870–1945. Amsterdam: Amsterdam University Press.
   Google Scholar
• O’Neal, L., Hale, T., & Cotten, S. (2011). The impact of information and communication technology (ICT) usage on depression among urban youth. Paper presented at the American Sociological Association Annual Meeting, Las Vegas, NV.
   Google Scholar
• Passel, J. S., & Cohn, D. (2008). U.S. Population projections: 2005–2050. Washington, DC: Pew Hispanic Center.
   Google Scholar
• Perez, T., Cromley, J., & Kaplan, A. (2014). The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, 106(1), 315–329. doi: 10.1037/a0034027
   Web of Science ®Google Scholar
• Robinson, L. (2009). A taste for the necessary. Information, Communication & Society, 12(4), 488–507. doi: 10.1080/13691180902857678
   Web of Science ®Google Scholar
• Robinson, L. (2012). Information-seeking 2.0: The effects of informational advantage. RESET – Social Science Research on the Internet, 1(1), 89–115.
   Google Scholar
• Robinson, L. (2014). Endowed, entrepreneurial, and empowered-strivers: Doing a lot with a lot, doing a lot with a little. Information, Communication & Society, 17(5), 521–536. doi: 10.1080/1369118X.2013.770049
   Web of Science ®Google Scholar
• Robinson, L., Cotten, S. R., Ono, H., Quan-Haase, A., Mesch, G., Chen, W., … Stern, M. J. (2015). Digital inequalities and why they matter. Information, Communication & Society, 18(5), 569–582. doi: 10.1080/1369118X.2015.1012532
   Web of Science ®Google Scholar
• Schradie, J. (2012). The trend of class, race, and ethnicity in social media inequality: Who still cannot afford to blog? Information, Communication & Society, 15(4), 555–571. doi: 10.1080/1369118X.2012.665939
   Web of Science ®Google Scholar
• Selwyn, N. (1997). Student’ attitudes toward computers: Validation of a computer attitude scale for 16–19 education. Computers & Education, 28(1), 35–41. doi: 10.1016/S0360-1315(96)00035-8
   Web of Science ®Google Scholar
• Simoni, Z. R., Gibson, P., Cotten, S. R., Stringer, K., & Coleman, L. O. (2016). Does place matter? The effects of concentrated poverty on the computer use of elementary students. Journal of Urban Technology, 23(3), 3–21. doi: 10.1080/10630732.2015.1073901
   Web of Science ®Google Scholar
• Simpkins, S., Davis-Kean, P., & Eccles, J. (2006). Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42(1), 70–83. doi: 10.1037/0012-1649.42.1.70
   PubMed Web of Science ®Google Scholar
• Smith, A. (2010). Mobile access 2010 pew Internet and American life project. Washington, DC: Taylor Francis.
   Google Scholar
• Trautwein, U., Marsh, H., Nagengast, B., Ludtke, O., Nagy, G., & Jonkmann, K. (2012). Probing for the multiplicative term in modern expectancy-value theory: A latent interation modeling study. Journal of Educational Psychology, 104(3), 763–777. doi: 10.1037/a0027470
   Web of Science ®Google Scholar
• Van Deursen, A., & Helsper, E. J. (2015). The third-level digital divide: Who benefits most from being online?. In L. Robinson, S. R. Cotten, J. Schulz, T. M. Hale & A. Williams (Eds.), Communication and information technologies annual (vol. 10, pp. 29–52). Emerald Group Publishing Limited.
   Google Scholar
• Van Deursen, A., & Van Dijk, J. (2013). The digital divide shifts to differences in usage. New Media Society, 16(3), 507–526. doi: 10.1177/1461444813487959
   Web of Science ®Google Scholar
• Van Dijk, J., &  Hacker, K. (2003). The digital divide as a complex and dynamic phenomenon. The Information Society, 19, 315–326. doi:10.1080/01972240390227895 doi: 10.1080/01972240309487
   Web of Science ®Google Scholar
• Vernadakis, N., Kouli, O., Tsitskari, E., Gioftsidou, A., & Antoniou, P. (2014). University students’ ability-expectancy beliefs and subjective task values for exergames. Computers & Education, 75, 149–161. doi: 10.1016/j.compedu.2014.02.010
   Web of Science ®Google Scholar
• Wigfield, A., & Cambria, J. (2010a). Expectancy-value theory: Retrospective and prospective. In T. Urdan & S. Karabenick (Eds.), The decade ahead: Theoretical perspectives on motivation and achievement (advances in motivation and achievement, volume 16 part A) (pp. 35–75). Emerald Group. Advance online publication.
   Google Scholar
• Wigfield, A., & Cambria, J. (2010b). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30, 1–35. doi: 10.1016/j.dr.2009.12.001
   Web of Science ®Google Scholar
• Wigfield, A., & Eccles, J. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. doi: 10.1006/ceps.1999.1015
   PubMed Web of Science ®Google Scholar
• Wigfield, A., Tonks, S., & Klauda, S. L. (2009). Expectancy-value theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation in school (pp. 55–76). New York, NY: Routledge.
   Google Scholar
• Xiang, P., McBride, R., Guan, J., & Solmon, M. (2003). Children's motivation in elementary physical education: An expectancy-value model of achievement choice. Research Quarterly for Exercise and Sport, 74(1), 25–35. doi: 10.1080/02701367.2003.10609061
   PubMed Web of Science ®Google Scholar
• Yager, J., Waitzkin, H., Parker, T., & Duran, B. (2007). Educating, training, and mentoring minority faculty and other trainees in mental health services research. Academic Psychiatry, 31(2), 146–151. doi: 10.1176/appi.ap.31.2.146
   PubMed Web of Science ®Google Scholar