Restricted accessResearch articleFirst published online 2019
Social-cognitive career theory variables as mediators for the relationship between deep learning and goal persistence in African American college students with disabilities
Education is one of the best pathways to middle class earnings. The Workforce Innovation and Opportunity Act requires state vocational rehabilitation agencies to reserve and expend at least 15% of its State allotment for pre-employment transition services to students with disabilities, including enrollment in comprehensive transition or postsecondary educational programs.
OBJECTIVE:
To evaluate the social-cognitive career theory (SCCT) variables of academic barriers coping self-efficacy, academic milestone self-efficacy, and academic outcome expectancy as mediators for the relationship between deep learning and goal persistence in a sample of African American college students with disabilities.
METHOD:
Quantitative descriptive research design using serial multiple mediation analysis (SMMA).
RESULTS:
SMMA results indicated that deep learning was significantly linked to goal persistence (total effect). Also, he direct effect between deep learning and goal persistence was still significant after controlling for the effects of the mediators, indicating deep learning is a strong predictor of goal persistence, and SCCT variables only partially mediated the relationship between deep learning and goal persistence. The mediation effects were estimates of the indirect effects for deep learning on goal persistence through (a) academic barriers coping self-efficacy, (b) academic milestone self-efficacy, (c) academic outcome expectancy, and (d) academic barriers coping self-efficacy, academic milestone self-efficacy and academic outcome expectancy.
CONCLUSION:
Findings of this study indicated that higher levels of deep learning had the benefit of helping African American college students with disabilities develop academic barriers coping self-efficacy, academic milestone self-efficacy and academic outcome expectancy, leading to goal persistence.
Betz,N. E. (2007) Career self-efficacy: Exemplary recent research and emerging directions. Journal of Career Assessment, 15, 403–422.
5.
Biggs,J. (1987) Student Approaches to Learning and Studying. Research Monograph. Australian Council for Educational Research Ltd., Radford House, Frederick St., Hawthorn 3122, Australia.
6.
Biggs,J., & Tang,C. (2007), Teaching for quality learning at university (3rd ed.).
Buckingham: Open University Press.
7.
Biggs,J., Kember,D., & Leung,D. Y. P. (2001) The revised two-factor Study Process Questionnaire: R-SPQ-F. British Journal of Educational Psychology, 71(1), 133–147.
8.
Byars-Winston,A., Estrada,Y., Howard,C., Davis,D., & Zalapa,J. (2011) Influence of social cognitive and ethnic variables on academic goals of underrepresented students in science and engineering: A multiple-groups analysis. Journal of Counseling Psychology, 57, 205–218.
9.
Cardoso,E., Dutta,A., Chiu,C. Y., Johnson,E. T., Kundu,M., & Chan,F. (2013) Social-cognitive predictors of STEM career interests and goal persistence in college students with disabilities from racial and ethnic minority backgrounds. Rehabilitation Research, Policy, & Education, 27, 271–284.
Chan,F., Iwanaga,K.,
Umucu,E., Yaghmaian,R., Wu,J.R., Bengtson,K., & Chen,X. (2017) Evidence-based practice and research utilization. In
TarvydasV. M. &
HartleyM. T. (Eds.), The professional practice of rehabilitation counseling
(pp. 359–380). New York: Springer Publishing Company.
12.
Cohen,J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.).
Hillsdale, NJ: Erlbaum.
13.
Compton,W. M., Gfroerer,J., Conway,K. P., & Finger,M. S. (2014) Unemployment and substance outcomes in the United States 2002–2010. Drug and Alcohol Dependence, 142, 350–353.
Ditchman,N., Wu,M., Chan,F., Fitzgerald,S., Lin,C. P., & Tu,W. M. (2013) Vocational rehabilitation. In
D.Strauser. (Ed.), Career development, employment, and disability in rehabilitation: From theory to practice, (pp. 343–360). New York: Springer Publishing Company.
16.
Entwistle,N. (2010) Taking stock: An overview of key research findings. In
HughesJ. C. and MightyJ. (Eds.), Taking stock: Research on teaching and learning in higher education. Kingston, Ontario: School of Policy Studies, Queens University.
17.
Entwistle,N. (1988), Styles of learning and teachingLondon, United Kingdom: David Fulton Publishers.
18.
Faul,F., Erdfelder,E., Lang,A. -G., & Buchner,A. (2007) G*Power: A flexible statistical power analysis for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191.
19.
Floyd,K., Harrington,S.Santiago,J.
(2009) The effect of engagement and perceived course value on deep and surface learning strategies. Informing Science: The International Journal of an Emerging Transdiscipline, 39, 181–190.
20.
Hackett,G., & Betz,N. E. (1981) A self-efficacy approach to the career development of women. Journal of Vocational Behavior,
18, 326–339.
21.
Haggis,T. (2003) Constructing images of ourselves? A critical investigation into ‘Approaches to Learning’ research in higher education. British Educational Research Journal, 29(1), 89–104.
22.
Hayes,A. F. (2013), Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press.
23.
Hayes,A. F. (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling. Retrieved from http://www.afhayes.com/public/process2012.pdf.
24.
Houghton,W. (2004) Engineering subject centre guide: Learning and teaching theory for engineering academics. Loughborough: HEA Engineering Subject Centre.
25.
Julian,T., & Kominski,R. (2011). Education and synthetic work-life earnings estimates. American Community Survey Reports, ACS-14. U.S. Census Bureau, Washington, DC.
26.
Kerr,B., & Kurpius,S. E. (2004). Encouraging talented girls in math and science: Effects of a guidance intervention. High Ability Studies, 15, 85–102.
Lent,R. W. (2001) Vocational psychology and career counseling: Inventing the future. Journal of Vocational Behavior, 59, 213–225.
29.
Lent,R. W. (2005). A social cognitive view of career development and counseling. In
BrownS. D. . &
LentR. W. . (Eds.), Career development and counseling: Putting theory and research to work, (pp. 101–127). New York: Wiley.
30.
Lent,R. W., Brown,S. D., & Hackett,G. (1994) Toward a unifying social cognitive theory of career and academic interest, choice, and performance [Monograph]. Journal of Vocational Behavior, 45, 79–122.
31.
Lent,R. W., Brown,S. D., & Hackett,G. (2000). Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counseling Psychology, 47, 36–49.
32.
Lent,R. W., Brown,S. D., & Larkin,K. C. (1986). Self-efficacy in the prediction of academic performance and perceived career options. Journal of Counseling Psychology, 47, 265–269.
33.
Lent,R. W., Brown,S. D., Schmidt,J., Brenner,B., Lyons,H., & Treistman,D. (2003). Relation of contextual supports and barriers to choice behavior in engineering majors: Test of alternative social cognitive models. Journal of Counseling Psychology, 50, 458–465.
34.
Lent,R. W., Brown,S. D., Sheu,H., Schmidt,J., Brenner,B. R., Gloster,C. S., & Treistman,D. (2005) Social cognitive predictors of academic interests and goals in engineering: Utility for women and students at historically Black universities. Journal of Counseling Psychology, 52, 84–92.
35.
Lent,R. W., Sheu,H. B., Singley,D., Schmidt,J. A., Schmidt,L. C., & Gloster,C. S. (2008) Longitudinal relations of self-efficacy to outcome expectations, interests, and major choice goals in engineering students. Journal of Vocational Behavior, 73, 328–335. 10.1016/j.jvb.2008.07.005.
36.
Marton,F., & Säljö,R. (1976) On qualitative differences in learning I: Outcome and process. British Journal of Educational Psychology, 46, 4–11.
37.
Marton,F.Säljö,R. (1997). Approaches to learning. In
Marton,F.Hounsell,D. & EntwistleN. (Eds.), The experience of learning, Edinburgh: Scottish Academic Press.
38.
Muller,V., Peebles,M. C., Chiu,C. Y., Iwanaga,K., Tang,X., Brooks,J., Eagle,D., & Chan,F. (2017) Association of employment and health and well-being in fibromyalgia. Journal of Rehabilitation,
83(2), 37–43.
39.
Mullikin,E. A., Bakken,L. L., & Betz,N. E. (2007) Assessing research self-efficacy in physician-scientists: The Clinical Research Appraisal Inventory. Journal of Career Assessment, 15, 367–387.
40.
O’Neill,J., Kang,H. -J., Sánchez,J., Muller,V., Aldrich,H., Pfaller,J., & Chan,F. (2015) Effect of college or university training on earnings of people with disabilities: A case control study. Journal of Vocational Rehabilitation, 43, 93–102.
Stanger-Hall,K. F. (2012) Multiple-choice exams: An obstacle for higher-level thinking in introductory science classes. Cell Biology Education—Life Sciences Education, 11(3), 294–306.
44.
Turner,S. L., & Lapan,R. T. (2005) Evaluation of an intervention to increase adolescents’ efficacy and interests in non-traditional careers. Journal of Vocational Behavior, 66(3), 516–531.
Weich,S., & Lewis,G. (1998) Poverty, unemployment, and common mental disorders: Population based cohort study. British Medical Journal, 66(3), 115–119.
