The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index.
Sustaining optimal motivation: A longitudinal analysis of interventions to broaden participation of underrepresented students in STEM.
Open accessAbstract
The underrepresentation of racial minorities and women in science, technology, engineering, and mathematics (STEM) disciplines is a national concern. Goal theory provides a useful framework from which to understand issues of underrepresentation. We followed a large sample of high-achieving African American and Latino undergraduates in STEM disciplines attending 38 institutions of higher education in the United States over 3 academic years. We report on the science-related environmental factors and person factors that influence the longitudinal regulation of goal orientations. Further, we examine how goal orientations in turn influence distal academic outcomes such as performance and persistence in STEM. Using SEM-based parallel process latent growth curve modeling, we found that (a) engagement in undergraduate research was the only factor that buffered underrepresented students against an increase in performance-avoidance goals over time; (b) growth in scientific self-identity exhibited a strong positive effect on growth in task and performance-approach goals over time; (c) only task goals positively influenced students' cumulative grade point average, over and above baseline grade point average; and (d) performance-avoidance goals predicted student attrition from the STEM pipeline. We discuss the implications of these findings for underrepresented students in STEM disciplines.
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Cites
Principles and Practice of Structural Equation Modeling
Tabri, N., & Elliott, C. M. (2012) · Canadian Graduate Journal of Sociology and Criminology · doi:10.15353/cgjsc-rcessc.v1i1.25 · Cited by 38,891
Hierarchical Linear Models: Applications and Data Analysis Methods.
Longford, N. T., Bryk, A. S., & Raudenbush, S. W. (1993) · Contemporary Sociology · doi:10.2307/2075823 · Cited by 18,724
Hierarchical Linear Models: Applications and Data Analysis Methods
Davidian, M. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s288 · Cited by 13,723
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
A social-cognitive approach to motivation and personality.
Dweck, C. S., & Leggett, E. L. (1988) · Psychological Review · doi:10.1037/0033-295x.95.2.256 · Cited by 9,745
Motivational and self-regulated learning components of classroom academic performance.
Pintrich, P. R., & De Groot, E. V. (1990) · Journal of Educational Psychology · doi:10.1037/0022-0663.82.1.33 · Cited by 8,332
Expectancy-value theory of achievement motivation
Wigfield, A., & Eccles, J. S. (2000) · Contemporary Educational Psychology · doi:10.1006/ceps.1999.1015 · Cited by 7,383
Motivational processes affecting learning.
Dweck, C. S. (1986) · American Psychologist · doi:10.1037/0003-066x.41.10.1040 · Cited by 6,971
Classrooms: Goals, structures, and student motivation.
Ames, C. (1992) · Journal of Educational Psychology · doi:10.1037/0022-0663.84.3.261 · Cited by 6,468
Digest of Education Statistics
(2014) · Choice Reviews Online · doi:10.5860/choice.51-5366 · Cited by 6,258
Cited by
Improving Underrepresented Minority Student Persistence in STEM
Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G., Hurtado, S., John, G. H., Matsui, J., McGee, R., et al. (2016) · CBE—Life Sciences Education · doi:10.1187/cbe.16-01-0038 · Cited by 793
Self-citationCourse-Based Undergraduate Research Experiences Can Make Scientific Research More Inclusive
Bangera, G., & Brownell, S. E. (2014) · CBE—Life Sciences Education · doi:10.1187/cbe.14-06-0099 · Cited by 722
“…experiences has been shown to increase interest in pursuing a PhD (Seymour et al., 2004; Russell et al., 2007) and seems to be particularly beneficial for students from histor ically underrepresented backgrounds (Villarejo et al., 2008; Jones et al., 2010; Espinosa, 2011; Hernandez et al., 2013).”
BackgroundRodenbusch, S. E., Hernandez, P. R., Simmons, S. L., & Dolan, E. L. (2016) · CBE—Life Sciences Education · doi:10.1187/cbe.16-03-0117 · Cited by 430
Self-citationPredicting academic performance: a systematic literature review
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018) · Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3293881.3295783 · Cited by 355
The Status of Women in STEM in Higher Education: A Review of the Literature 2007–2017
Blackburn, H. (2017) · Science & Technology Libraries · doi:10.1080/0194262x.2017.1371658 · Cited by 353
Metadata
- Authors
- Hernandez, P. R., Schultz, P. W., Estrada, M., Woodcock, A., & Chance, R. C.
- Venue
- Journal of Educational Psychology
- Published
- 2013-02
- Volume
- 105
- Issue
- 1
- Pages
- 89-107
- ISSN
- 0022-0663 (ISSN-L)
- 1939-2176
- OpenAlex
- W1994285147
- Cited by
- 337
- References
- 114
- FWCI
- 27.01
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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