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
STEM participation, affect detection, knowledge modeling, educational data mining
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.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
- Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons 4.0 License (Attribution-Noncommercial-No Derivatives 4.0 International), or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
- Noncommercial—other users (including Publisher) may not use this Work for commercial purposes;
- No Derivative Works—other users (including Publisher) may not alter, transform, or build upon this Work,with the understanding that any of the above conditions can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- the Work is the Author’s original work;
- the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- the Work is not pending review or under consideration by another publisher;
- the Work has not previously been published;
- the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- the Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.