Predicting Student Participation in STEM Careers: The Role of Affect and Engagement during Middle School

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Aug 23, 2020
Ma Victoria Quintos Almeda Ryan Shaun Baker

Abstract

Given the increasing need for skilled workers in science, technology, engineering, and mathematics (STEM), there is a burgeoning interest to encourage young students to pursue a career in STEM fields. Middle school is an opportune time to guide students' interests towards STEM disciplines, as they begin to think about and plan for their career aspirations. Previous studies have shown that detectors of students' learning, affect, and engagement, measured from their interactions within an online tutoring system during middle school, are strongly predictive of their eventual choice to attend college and enroll in a STEM major (San Pedro et al., 2013; 2014). In this study, we extend prior work by examining how the constructs measured by these detectors relate to the decision to participate in a STEM career after college. Findings from this study suggest that subtle forms of disengagement (i.e., gaming the system, carelessness) are predictive and can potentially provide actionable information for teachers and counselors to apply early intervention in STEM learning. In general, this study sheds light on the relevant student factors that influence STEM participation years later, providing a more comprehensive understanding of student STEM trajectories.

How to Cite

Almeda, M. V. Q., & Baker, R. S. (2020). Predicting Student Participation in STEM Careers: The Role of Affect and Engagement during Middle School. JEDM | Journal of Educational Data Mining, 12(2), 33-47. https://doi.org/10.5281/zenodo.4008054
Abstract 41 | PDF Downloads 26

##plugins.themes.bootstrap3.article.details##

Keywords

STEM participation, affect detection, knowledge modeling, educational data mining

References
ARROYO, I., FERGUSON, K., JOHNS, J., DRAGON, T., MEHERANIAN, H., FISHER, D., ... & WOOLF, B. P. (2007, June). Repairing disengagement with non-invasive interventions. In AIED (Vol. 2007, pp. 195-202).

BAKER, R. S., CORBETT, A. T., GOWDA, S. M., WAGNER, A. Z., MACLAREN, B. A., KAUFFMAN, L. R., ... & GIGUERE, S. (2010, June). Contextual slip and prediction of student performance after use of an intelligent tutor. In International Conference on User Modeling, Adaptation, and Personalization (pp. 52-63). Springer, Berlin, Heidelberg.

BAKER, R. S., CORBETT, A. T., KOEDINGER, K. R., EVENSON, S., ROLL, I., WAGNER, A. Z., ... & BECK, J. E. (2006, June). Adapting to when students game an intelligent tutoring system. In International Conference on Intelligent Tutoring Systems (pp. 392-401). Springer, Berlin, Heidelberg.

BALFANZ, R., HERZOG, L., & MAC IVER, D. J. (2007). Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions. Educational Psychologist, 42(4), 223-235.

BANDURA, A., BARBARANELLI, C., CAPRARA, G. V., & PASTORELLI, C. (2001). Self‐efficacy beliefs as shapers of children's aspirations and career trajectories. Child Development, 72(1), 187-206.

BENJAMINI, Y., & HOCHBERG, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289-300.

BLEEKER, M. M., & JACOBS, J. E. (2004). Achievement in math and science: Do mothers' beliefs matter 12 years later?. Journal of Educational Psychology, 96(1), 97.

CHEN, X., & WEKO, T. (2009). Students who study science, technology, engineering, and mathematics (STEM) in postsecondary education (NCES 2009-61). Washington, DC: National Center for Education Statistics.

CLEMENTS, M. A. (1982). Careless errors made by sixth-grade children on written mathematical tasks. Journal for Research in Mathematics Education, 136-144.

COCEA, M., HERSHKOVITZ, A., & BAKER, R. S. (2009). The impact of off-task and gaming behaviors on learning: immediate or aggregate?. In Proceeding of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling. IOS Press.

CORBETT, A. T., & ANDERSON, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253-278.

CRISP, G., NORA, A., & TAGGART, A. (2009). Student characteristics, pre-college, college, and environmental factors as predictors of majoring in and earning a STEM degree: An analysis of students attending a Hispanic Serving Institution. American Educational Research Journal, 46(4), 924–942.

GRUCA, J. M., ETHINGTON, C. A., & PASCARELLA, E. T. (1988). Intergenerational effects of college graduation on career sex atypicality in women. Research in Higher Education, 29(2), 99–124.

HEFFERNAN, N. T., & HEFFERNAN, C. L. (2014). The ASSISTments Ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470-497.

HOLSTEIN, K., MCLAREN, B. M., & ALEVEN, V. (2018). Informing the design of teacher awareness tools through causal alignment analysis. In J. Kay and R. Luckin (Eds.). Proceedings of the 13th International Conference of the Learning Sciences (pp. 104-111).

KNEZEK, G., CHRISTENSEN, R., TYLER-WOOD, T., & PERIATHIRUVADI, S. (2013). Impact of Environmental Power Monitoring Activities on Middle School Student Perceptions of STEM. Science Education International, 24(1), 98-123.

LENT, R., BROWN, S. & HACKETT, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice and performance. Journal of Vocational Behavior 45, 79–122.

LENT, R., BROWN, S. & HACKETT, G. (2000). Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counseling Psychology 47, 36–49.

NATIONAL SCIENCE FOUNDATION, NATIONAL CENTER FOR SCIENCE AND ENGINEERING STATISTICS. (2015). Science and Engineering Degrees: 1966-2012. Detailed Statistical Tables NSF 15-326. Arlington. V.A. Available at https://www.nsf.gov/statistics/2015/nsf15326/#field

OCUMPAUGH, J. (2015). Baker Rodrigo Ocumpaugh monitoring protocol (BROMP) 2.0 technical and training manual. New York, NY and Manila, Philippines: Teachers College, Columbia University and Ateneo Laboratory for the Learning Sciences.

PARDOS, Z. A., BAKER, R. S., SAN PEDRO, M. O., GOWDA, S. M., & GOWDA, S. M. (2013, April). Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 117-124). ACM.

PERIN, A. & ANDERSON, M. (2019, April 10). Share of U.S. Adults using social media including Facebook, is mostly unchaged since 2018 [Web log post]. Retrieved January 7, 2019. from https://pewrsr.ch/2VxJuJ3.

PENG, C. Y. J., LEE, K. L., & INGERSOLL, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96(1), 3-14.

PRESIDENT'S COUNCIL OF ADVISORS ON SCIENCE AND TECHNOLOGY (PCAST). (2012). Report to the President: Engage to Excel: Producing One Million Additional College Graduates with Degrees in Science, Technology, Engineering, and Mathematics. Available at http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-engage-to-excel-final_2-25-12.pdf.

REINHOLD, S., HOLZBERGER, D., & SEIDEL, T. (2018). Encouraging a career in science: a research review of secondary schools' effects on students' STEM orientation. Studies in Science Education, 54(1), 69-103.

RUIZ, E.C. (2012). Research summary: Setting higher expectations: Motivating middle graders to succeed. Retrieved December 24, 2018 from http://www.amle.org/TabId/270/ArtMID/888/ArticleID/307/Research-Summary-Setting-Higher-Expectations.aspx/

SAN PEDRO, M. O., BAKER, R., BOWERS, A., & HEFFERNAN, N. (2013, July). Predicting college enrollment from student interaction with an intelligent tutoring system in middle school. In S. K. D'Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (pp 171-184).

SAN PEDRO, M. O., OCUMPAUGH, J., BAKER, R. S., & HEFFERNAN, N. T. (2014). Predicting STEM and Non-STEM College Major Enrollment from Middle School Interaction with Mathematics Educational Software. In Proceedings of the 7th International Conference on Educational Data Mining (pp. 276-279).

U.S. DEPARTMENT OF EDUCATION, NATIONAL CENTER FOR EDUCATION STATISTICS. (2019). The Condition of Education 2019 (NCES 2019-144), Undergraduate Retention and Graduation Rates.

U.S. DEPARTMENT OF LABOR, BUREAU OF LABOR STATISTICS. (2014). Chapter 2: Higher Education in Science and Engineering. Available at https://www.nsf.gov/statistics/seind14/index.cfm/chapter-2/c2s2.htm

VAN TUIJL, C., & VAN DER MOLEN, J. H. W. (2016). Study choice and career development in STEM fields: an overview and integration of the research. International Journal of Technology and Design Education, 26(2), 159-183.

VINCENT, K. B., KASPERSKI, S. J., CALDEIRA, K. M., GARNIER-DYKSTRA, L. M., PINCHEVSKY, G. M.,O'GRADY, K. E., & ARRIA, A. M. (2012). Maintaining superior follow-up rates in a longitudinal study: Experiences from the College Life Study. International journal of multiple research approaches, 6(1), 56-72.

WANG, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081-1121.
Section
Special Issue on ASSISTments Longitudinal Data