Analysis of Click-Stream Data to Predict STEM Careers from Student Usage of an Intelligent Tutoring System
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Published
Aug 22, 2020
Jihed Makhlouf
Tsunenori Mine
Abstract
In recent years, we have seen the continuous and rapid increase of job openings in Science, Technology, Engineering and Math (STEM)-related fields. Unfortunately, these positions are not met with an equal number of workers ready to fill them. Efforts are being made to find durable solutions for this phenomena, and they start by encouraging young students to enroll in STEM college majors. However, enrolling in a STEM major requires specific skills in math and science that are learned in schools. Hopefully, institutions are adopting educational software that collects data from the students' usage. This gathered data will serve to conduct analysis and detect students' behaviors, predict their performances and their eventual college enrollment. As we will outline in this paper, we used data collected from the students' usage of an Intelligent Tutoring System to predict whether they would pursue a career in STEM-related fields. We conducted different types of analysis called "problem-based approach" and "skill-based approach". The problem- based approach focused on evaluating students' actions based on the problems they solved. Likewise, in the skill-based approach we evaluated their usage based on the skills they had practiced. Furthermore, we investigated whether comparing students' features with those of their peer schoolmates can improve the prediction models in both the skill-based and the problem-based approaches. The experimental re- sults showed that the skill-based approach with school aggregation achieved the best results with regard to a combination of two metrics which are the Area Under the Receiver Operating Characteristic Curve (AUC) and the Root Mean Squared Error (RMSE).
How to Cite
Makhlouf, J., & Mine, T. (2020). Analysis of Click-Stream Data to Predict STEM Careers from Student Usage of an Intelligent Tutoring System. Journal of Educational Data Mining, 12(2), 1–18. https://doi.org/10.5281/zenodo.4008050
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Keywords
STEM career, predictive analytics, educational data mining, intelligent tutoring system
References
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CHUN-KIT, Y. AND DIT-YAN, Y. 2018. Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale. ACM, New York, NY, USA, 5:1–5:10.
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CORBETT, A. T. AND ANDERSON, J. R. 1995. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 4 (Dec), 253–278.
EFFAT, F., MAAZ, S. K., WENJIA, C., AND COLLIN, F. L. 2018. Predicting post-college STEM career entrance from middle school clickstream data. https://drive.google.com/file/d/ 1dLOYDtQsl1C1kClKdbPrPvEr8eZ2kjik/view. Workshop on the Scientific Findings from the ASSISTments Longitudinal Data Competition in the 11th International Conference on Educational Data Mining, Buffalo, NY, USA.
JOEL, M. AND KURT, V. 1995. Student assessment using Bayesian nets. International Journal of Human Computer Studies 42, 6 (6), 575–591.
MAKHLOUF, J. AND MINE, T. 2018. Investigating how school-aggregated data can influence in predicting STEM careers from student usage of an intelligent tutoring sySTEM. https://drive.google. com/file/d/1XS1spxOdbFkFfRsjTAo-r0_hTTe6mkTO/view. Workshop on the Scientific Findings from the ASSISTments Longitudinal Data Competition in the 11th International Conference on Educational Data Mining, Buffalo, NY, USA.
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OLENCHAK, F. AND HBERT, T. 2002. Endangered academic talent: Lessons learned from gifted firstgeneration college males. Journal of College Student Development 43, 2 (03), 195–212.
OLSON, R. S., BARTLEY, N., URBANOWICZ, R. J., AND MOORE, J. H. 2016. Evaluation of a treebased pipeline optimization tool for automating data science. In Proceedings of the Genetic and Evolutionary Computation Conference 2016. ACM, New York, NY, USA, 485–492.
PARDOS, Z. A., BAKER, R. S., SAN PEDRO, M. O., GOWDA, S. M., AND GOWDA, S. M. 2013. 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. ACM, New York, NY, USA, 117–124.
PASCARELLA, E. T., PIERSON, C. T., WOLNIAK, G. C., AND TERENZINI, P. T. 2004. First-generation college students: Additional evidence on college experiences and outcomes. Journal of Higher Education 75, 3 (5), 249–284.
PAVLIK, P. I., CEN, H., AND KOEDINGER, K. R. 2009. Performance factors analysis – a new alternative to knowledge tracing. In Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning SySTEMs That Care: From Knowledge Representation to Affective Modelling. IOS Press, Amsterdam, The Netherlands, 531–538.
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SABOURIN, J., MOTT, B., AND LESTER, J. C. 2011. Modeling learner affect with theoretically grounded dynamic Bayesian networks. In Affective Computing and Intelligent Interaction. Springer Berlin Heidelberg, Berlin, Heidelberg, 286–295.
SAN PEDRO, M. O., BAKER, R. S., BOWERS, A., AND HEFFERNAN, N. T. 2013. Predicting college enrollment from student interaction with an intelligent tutoring sySTEM in middle school. In Proceedings of the 6th International Conference on Educational Data Mining. 177–184.
SAN PEDRO, M. O., BAKER, R. S., AND RODRIGO, M. M. T. 2011. Detecting carelessness through contextual estimation of slip probabilities among students using an intelligent tutor for mathematics. In Proceedings of the 15th International Conference on Artificial Intelligence in Education. Springer Berlin Heidelberg, Berlin, Heidelberg, 304–311.
SAN PEDRO, M. O., OCUMPAUGH, J., BAKER, R. S., AND 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. 276–279.
THANAPORN, P., HEFFERNAN, N. T., AND BAKER, R. S. 2018. Assistments longitudinal data mining competition 2017: A preface. https://drive.google.com/file/d/ 1Dt6xhFHTqpqvJp9rOcfc2X-3l2l9gG1J/view. Workshop on the Scientific Findings from the ASSISTments Longitudinal Data Competition in the 11th International Conference on Educational Data Mining, Buffalo, NY, USA.
WHALEN, D. F. AND S HELLEY II, M. C. 2010. Academic success for STEM and non-STEM. Journal of STEM Education: Innovations and Research 11, 1, 45 – 60.
XUELI, W. 2012. Modeling student choice of STEM fields of study: Testing a conceptual framework of motivation, high school learning, and postsecondary context of support. WISCAPE Working Paper. Wisconsin Center for the Advancement of Postsecondary Education.
XUELI, W. 2013. Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal 50, 5, 1081–1121.
BAKER, R. S., CORBETT, A. T., AND KOEDINGER, K. R. 2004. Detecting student misuse of intelligent tutoring sySTEMs. In Intelligent Tutoring SySTEMs. Springer Berlin Heidelberg, Berlin, Heidelberg, 531–540.
BALFANZ, R. 2009. Putting Middle Grades Students on the Graduation Path A Policy and Practice Brief. National Middle School Association, Westerville, OH.
CHUN-KIT, Y. AND DIT-YAN, Y. 2018. Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale. ACM, New York, NY, USA, 5:1–5:10.
CHUN-KIT, Y., ZIZHENG, L., KAI, Y., AND DIT-YAN, Y. 2018. Incorporating features learned by an enhanced deep knowledge tracing model for STEM/non-STEM job prediction. https://drive. google.com/file/d/1OSKLK5lXUHFGEPKfsbUhB3BRHvU-vMkk/view. Workshop on the Scientific Findings from the ASSISTments Longitudinal Data Competition in the 11th International Conference on Educational Data Mining, Buffalo, NY, USA.
CORBETT, A. T. AND ANDERSON, J. R. 1995. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 4 (Dec), 253–278.
EFFAT, F., MAAZ, S. K., WENJIA, C., AND COLLIN, F. L. 2018. Predicting post-college STEM career entrance from middle school clickstream data. https://drive.google.com/file/d/ 1dLOYDtQsl1C1kClKdbPrPvEr8eZ2kjik/view. Workshop on the Scientific Findings from the ASSISTments Longitudinal Data Competition in the 11th International Conference on Educational Data Mining, Buffalo, NY, USA.
JOEL, M. AND KURT, V. 1995. Student assessment using Bayesian nets. International Journal of Human Computer Studies 42, 6 (6), 575–591.
MAKHLOUF, J. AND MINE, T. 2018. Investigating how school-aggregated data can influence in predicting STEM careers from student usage of an intelligent tutoring sySTEM. https://drive.google. com/file/d/1XS1spxOdbFkFfRsjTAo-r0_hTTe6mkTO/view. Workshop on the Scientific Findings from the ASSISTments Longitudinal Data Competition in the 11th International Conference on Educational Data Mining, Buffalo, NY, USA.
NOONAN, R. 2017. Stem jobs: 2017 update. Office of the Chief Economist, Economics and Statistics Administration, U.S. Department of Commerce(ESA Issue Brief 02-17).
OLENCHAK, F. AND HBERT, T. 2002. Endangered academic talent: Lessons learned from gifted firstgeneration college males. Journal of College Student Development 43, 2 (03), 195–212.
OLSON, R. S., BARTLEY, N., URBANOWICZ, R. J., AND MOORE, J. H. 2016. Evaluation of a treebased pipeline optimization tool for automating data science. In Proceedings of the Genetic and Evolutionary Computation Conference 2016. ACM, New York, NY, USA, 485–492.
PARDOS, Z. A., BAKER, R. S., SAN PEDRO, M. O., GOWDA, S. M., AND GOWDA, S. M. 2013. 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. ACM, New York, NY, USA, 117–124.
PASCARELLA, E. T., PIERSON, C. T., WOLNIAK, G. C., AND TERENZINI, P. T. 2004. First-generation college students: Additional evidence on college experiences and outcomes. Journal of Higher Education 75, 3 (5), 249–284.
PAVLIK, P. I., CEN, H., AND KOEDINGER, K. R. 2009. Performance factors analysis – a new alternative to knowledge tracing. In Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning SySTEMs That Care: From Knowledge Representation to Affective Modelling. IOS Press, Amsterdam, The Netherlands, 531–538.
PIECH, C., BASSEN, J., HUANG, J., GANGULI, S., SAHAMI, M., GUIBAS, L. J., AND SOHL-DICKSTEIN, J. 2015. Deep knowledge tracing. In Advances in Neural Information Processing SySTEMs 28, C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds. Curran Associates, Inc., 505–513.
REYE, J. 2004. Student modelling based on belief networks. International Journal of Artificial Intelligence in Education 14, 1 (jan), 63–96.
RUITAO, L. AND AIXIN, T. 2018. Stem career prediction using an automatic machine learning approach. https://drive.google.com/file/d/1ps_LX8mDSdnyY79FqCczlb8igqgX1JPA/view. Workshop on the Scientific Findings from the ASSISTments Longitudinal Data Competition in the 11th International Conference on Educational Data Mining, Buffalo, NY, USA.
SABOURIN, J., MOTT, B., AND LESTER, J. C. 2011. Modeling learner affect with theoretically grounded dynamic Bayesian networks. In Affective Computing and Intelligent Interaction. Springer Berlin Heidelberg, Berlin, Heidelberg, 286–295.
SAN PEDRO, M. O., BAKER, R. S., BOWERS, A., AND HEFFERNAN, N. T. 2013. Predicting college enrollment from student interaction with an intelligent tutoring sySTEM in middle school. In Proceedings of the 6th International Conference on Educational Data Mining. 177–184.
SAN PEDRO, M. O., BAKER, R. S., AND RODRIGO, M. M. T. 2011. Detecting carelessness through contextual estimation of slip probabilities among students using an intelligent tutor for mathematics. In Proceedings of the 15th International Conference on Artificial Intelligence in Education. Springer Berlin Heidelberg, Berlin, Heidelberg, 304–311.
SAN PEDRO, M. O., OCUMPAUGH, J., BAKER, R. S., AND 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. 276–279.
THANAPORN, P., HEFFERNAN, N. T., AND BAKER, R. S. 2018. Assistments longitudinal data mining competition 2017: A preface. https://drive.google.com/file/d/ 1Dt6xhFHTqpqvJp9rOcfc2X-3l2l9gG1J/view. Workshop on the Scientific Findings from the ASSISTments Longitudinal Data Competition in the 11th International Conference on Educational Data Mining, Buffalo, NY, USA.
WHALEN, D. F. AND S HELLEY II, M. C. 2010. Academic success for STEM and non-STEM. Journal of STEM Education: Innovations and Research 11, 1, 45 – 60.
XUELI, W. 2012. Modeling student choice of STEM fields of study: Testing a conceptual framework of motivation, high school learning, and postsecondary context of support. WISCAPE Working Paper. Wisconsin Center for the Advancement of Postsecondary Education.
XUELI, W. 2013. Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal 50, 5, 1081–1121.
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