Modeling NAEP Test-Taking Behavior Using Educational Process Analysis
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Published
Aug 26, 2021
Nirmal Patel
Aditya Sharma
Tirth Shah
Derek Lomas
https://orcid.org/0000-0003-1472-4029
Abstract
Process Analysis is an emerging approach to discover meaningful knowledge from temporal educational data. The study presented in this paper shows how we used Process Analysis methods on the National Assessment of Educational Progress (NAEP) test data for modeling and predicting student test-taking behavior. Our process-oriented data exploration gave us insightful findings of how students were interacting with the digital assessment system over time. To discover what processes students were following during the NAEP Digital Assessment, we first developed an innovative set of research questions. Then, we used Process Analysis methods to answer these questions and created a set of features that described student behavior over time. These features were used to create an ensemble model that aimed to accurately predict the digital test-taking efficiency of the students taking NAEP. Our model emerged as one of the most successful models in the 2019 NAEP Data Mining Competition, scoring second place out of 89 teams.
How to Cite
Patel, N., Sharma, A., Shah, T., & Lomas, D. (2021). Modeling NAEP Test-Taking Behavior Using Educational Process Analysis. Journal of Educational Data Mining, 13(2), 16–54. https://doi.org/10.5281/zenodo.5275312
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Keywords
process analysis, behavior modeling, test-taking behavior, curriculum pacing, process mining
References
ALEVEN, V. AND KOEDINGER, K. R. 2000. Limitations of student control: Do students know when they need help? In Proceedings of the 5th International Conference on Intelligent Tutoring Systems, G. Gauthier, C. Frasson, and K. VanLehn, Eds. Springer, Berlin, Heidelberg, 292-303.
ALEVEN, V., MCLAREN, B., ROLL, I., AND KOEDINGER, K. 2006. Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence in Education 16, 2, 101-128.
BAKER, R., WOOLF, B., KATZ, I., FORSYTH, C., AND OCUMPAUGH, J. 2019. Nation's Report Card Data Mining Competition 2019. https://sites.google.com/view/dataminingcompetition2019/home.
BAKER, R. S. 2015. Behavior Detection, Big Data and Education, 2nd ed. Teachers College, Columbia University, New York, NY, Chapter 3.
BAKER, R. S., CORBETT, A. T., AND KOEDINGER, K. R. 2004. Detecting student misuse of intelligent tutoring systems. In Proceedings of the 7th International Conference on Intelligent Tutoring Systems, J. C. Lester, R. M. Vicari, and F. Paraguacu, Eds. Lecture Notes in Computer Science, vol. 3220. Springer, 531-540.
BAKER, R. S., CORBETT, A. T., KOEDINGER, K. R., EVENSON, S., ROLL, I., WAGNER, A. Z., NAIM, M., RASPAT, J., BAKER, D. J., AND BECK, J. E. 2006. Adapting to when students game an intelligent tutoring system. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems, M. Ikeda, K. D. Ashley, and T.-W. Chan, Eds. Springer, 392-401.
BAKER, R. S., CORBETT, A. T., KOEDINGER, K. R., AND WAGNER, A. Z. 2004. Off-task behavior in the Cognitive Tutor classroom: When students "game the system". In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, E. Dykstra-Erickson and M. Tscheligi, Eds. ACM, 383-390.
BAKER, R. S. J. D . 2007. Modeling and understanding students' off-task behavior in intelligent tutoring systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, M. B. Rosson and D. J. Gilmore, Eds. ACM, 1059-1068.
BERGNER, Y. AND VONDAVIER, A. A. 2019. Process data in NAEP: Past, present, and future. Journal of Educational and Behavioral Statistics 44, 6, 706-732.
BOGARIN, A., CEREZO, R., AND ROMERO, C. 2018. A survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8, 1, e1230.
BOGARIN, A., ROMERO, C., CEREZO, R., AND SANCHEZ-SANTILLAN, M. 2014. Clustering for improving educational process mining. In Proceedings of the 4th Learning Analytics and Knowledge Conference, M. D. Pistilli, J. Willis, D. Koch, K. E. Arnold, S. D. Teasley, and A. Pardo, Eds. ACM, 11-15.
CETINTAS, S., SI, L., XIN, Y. P. P., AND HORD, C. 2009. Automatic detection of off-task behaviors in intelligent tutoring systems with machine learning techniques. IEEE Transactions on Learning Technologies 3, 3, 228-236.
DATA QUALITY CAMPAIGN. 2018. What parents and teachers think about educational data. https://dataqualitycampaign.org/resource/what-parents-and-teachers-think-abouteducation-data/.
GARCIA, E., ROMERO, C., VENTURA, S., DEC ASTRO, C., AND CALDERS, T. 2010. Association rule mining in learning management systems. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, Boca Raton, FL, Chapter 7, 93-106.
GOLDHAMMER, F., MARTENS, T., CHRISTOPH, G., AND LUDTKE, O. 2016. Test-taking engagement in PIAAC. Tech. Rep. 133, Organisation for Economic Cooperation and Development, Paris.
GUNTHER, C. W. AND VANDERAALST, W. M. P. 2007. Fuzzy mining-adaptive process simplification based on multi-perspective metrics. In International Conference on Business Process Management. Springer, 328-343.
GUO, H., RIOS, J. A., HABERMAN, S., LIU, O. L., WANG, J., AND PAEK, I. 2016. A new procedure for detection of students' rapid guessing responses using response time. Applied Measurement in Education 29, 3, 173-183.
HOLSTEIN, K., MCLAREN, B. M., AND ALEVEN, V. 2018. Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In Proceedings of the 19th International Conference on Artificial Intelligence in Education, C. P. Rose, R. M. Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. M. McLaren, and B. du Boulay, Eds. Lecture Notes in Computer Science, vol. 10947. Springer, 154-168.
KONG, X. J., WISE, S. L., AND BHOLA, D. S. 2007. Setting the response time threshold parameter to differentiate solution behavior from rapid-guessing behavior. Educational and Psychological Measurement 67, 4, 606-619.
KUHN, M. AND JOHNSON, K. 2013a. Applied Predictive Modeling. Springer, New York, NY.
KUHN, M. AND JOHNSON, K. 2013b. An introduction to feature selection. In Applied Predictive Modeling. Springer, New York, NY, 487-519.
LEE, Y.-H. AND JIA, Y. 2014. Using response time to investigate students' test-taking behaviors in a NAEP computer-based study. Large-scale Assessments in Education 2, 1, 1-24.
LYNCH, C. F., BARNES, T., XUE, L., AND GITINABARD, N. 2017. Graph-based educational data mining. In Proceedings of the 10th International Conference on Educational Data Mining, X. Hu, T. Barnes, A. Hershkovitz, and L. Paquette, Eds. International Educational Data Mining Society.
PATEL, N., SELLMAN, C., AND LOMAS, D. 2017. Mining frequent learning pathways from a large educational dataset. arXiv preprint arXiv:1705.11125.
PATEL, N., SHARMA, A., SELLMAN, C., AND LOMAS, D. 2018. Curriculum pacing: A new approach to discover instructional practices in classrooms. In Proceedings of the 14th International Conference on Intelligent Tutoring Systems, R. Nkambou, R. Azevedo, and J. Vassileva, Eds. Lecture Notes in Computer Science, vol. 10858. Springer, 345-351.
PATIKORN, M. AND HEFFERNAN, N. 2019. Dataset. https://sites.google.com/view/dataminingcompetition2019/dataset#h.pndPGLzSaEKFb.
PEDRO, M. O. C. Z. S., BAKER, R. S. J., 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, G. Biswas, S. Bull, J. Kay, and A. Mitrovic, Eds. Lecture Notes in Computer Science, vol. 6738. Springer, 304-311.
PEDRO, M. O. C. Z. S., RODRIGO, M. M. T., AND BAKER, R. S. J. 2011. The relationship between carelessness and affect in a Cognitive Tutor. In Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, S. K. D'Mello, A. C. Graesser, B. W. Schuller, and J. Martin, Eds. Lecture Notes in Computer Science, vol. 6974. Springer, 306-315.
ROWE, J. P., MCQUIGGAN, S. W., ROBISON, J. L., AND LESTER, J. C. 2009. Off-task behavior in narrative-centered learning environments. In Proceedings of the 14th International Conference on Artificial Intelligence in Education, V. Dimitrova, R. Mizoguchi, B. du Boulay, and A. C. Graesser, Eds. Frontiers in Artificial Intelligence and Applications, vol. 200. IOSPress, 99-106.
SAHIN, F. 2019. Exploring the relations between students' time management strategies and test performance. Paper presented at the Annual meeting of the National Council for Measurement in Education.
SAHIN, F. AND COLVIN, K. F. 2020. Enhancing response time thresholds with response behaviors for detecting disengaged examinees. Large-scale Assessments in Education 8, 5, 1-24.
SLATTOW, G. 1977. Demonstration of the PLATO IV computer-based education system. Final report. January 1, 1972-June 30, 1976. Tech. rep., University of Illinois.
TRCKA, N., PECHENIZKIY, M., AND VANDERAALST, W. 2010. Process mining from educational data. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, Boca Raton, FL, Chapter 9, 123-142.
WALONOSKI, J. A. AND HEFFERNAN, N. T. 2006. Detection and analysis of off-task gaming behavior in intelligent tutoring systems. In Intelligent Tutoring Systems, M. Ikeda, K. Ashley, and T.-W. Chan, Eds. Lecture Notes in Computer Science, vol. 4053. Springer Berlin Heidelberg, 382-391.
WIXON, M., BAKER, R. S. J., GOBERT, J. D., OCUMPAUGH, J., AND BACHMANN, M. 2012. WTF? Detecting students who are conducting inquiry without thinking fastidiously. In Proceedings of the 20th International Conference on User Modeling, Adaptation, and Personalization, J. Masthoff, B. Mobasher, M. C. Desmarais, and R. Nkambou, Eds. Lecture Notes in Computer Science, vol. 7379. Springer, 286-296.
ZHOU, M., XU, Y., NESBIT, J. C., AND WINNE, P. H. 2010. Sequential pattern analysis of learning logs: Methodology and applications. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, Boca Raton, FL, Chapter 8, 107-121.
ALEVEN, V., MCLAREN, B., ROLL, I., AND KOEDINGER, K. 2006. Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence in Education 16, 2, 101-128.
BAKER, R., WOOLF, B., KATZ, I., FORSYTH, C., AND OCUMPAUGH, J. 2019. Nation's Report Card Data Mining Competition 2019. https://sites.google.com/view/dataminingcompetition2019/home.
BAKER, R. S. 2015. Behavior Detection, Big Data and Education, 2nd ed. Teachers College, Columbia University, New York, NY, Chapter 3.
BAKER, R. S., CORBETT, A. T., AND KOEDINGER, K. R. 2004. Detecting student misuse of intelligent tutoring systems. In Proceedings of the 7th International Conference on Intelligent Tutoring Systems, J. C. Lester, R. M. Vicari, and F. Paraguacu, Eds. Lecture Notes in Computer Science, vol. 3220. Springer, 531-540.
BAKER, R. S., CORBETT, A. T., KOEDINGER, K. R., EVENSON, S., ROLL, I., WAGNER, A. Z., NAIM, M., RASPAT, J., BAKER, D. J., AND BECK, J. E. 2006. Adapting to when students game an intelligent tutoring system. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems, M. Ikeda, K. D. Ashley, and T.-W. Chan, Eds. Springer, 392-401.
BAKER, R. S., CORBETT, A. T., KOEDINGER, K. R., AND WAGNER, A. Z. 2004. Off-task behavior in the Cognitive Tutor classroom: When students "game the system". In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, E. Dykstra-Erickson and M. Tscheligi, Eds. ACM, 383-390.
BAKER, R. S. J. D . 2007. Modeling and understanding students' off-task behavior in intelligent tutoring systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, M. B. Rosson and D. J. Gilmore, Eds. ACM, 1059-1068.
BERGNER, Y. AND VONDAVIER, A. A. 2019. Process data in NAEP: Past, present, and future. Journal of Educational and Behavioral Statistics 44, 6, 706-732.
BOGARIN, A., CEREZO, R., AND ROMERO, C. 2018. A survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8, 1, e1230.
BOGARIN, A., ROMERO, C., CEREZO, R., AND SANCHEZ-SANTILLAN, M. 2014. Clustering for improving educational process mining. In Proceedings of the 4th Learning Analytics and Knowledge Conference, M. D. Pistilli, J. Willis, D. Koch, K. E. Arnold, S. D. Teasley, and A. Pardo, Eds. ACM, 11-15.
CETINTAS, S., SI, L., XIN, Y. P. P., AND HORD, C. 2009. Automatic detection of off-task behaviors in intelligent tutoring systems with machine learning techniques. IEEE Transactions on Learning Technologies 3, 3, 228-236.
DATA QUALITY CAMPAIGN. 2018. What parents and teachers think about educational data. https://dataqualitycampaign.org/resource/what-parents-and-teachers-think-abouteducation-data/.
GARCIA, E., ROMERO, C., VENTURA, S., DEC ASTRO, C., AND CALDERS, T. 2010. Association rule mining in learning management systems. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, Boca Raton, FL, Chapter 7, 93-106.
GOLDHAMMER, F., MARTENS, T., CHRISTOPH, G., AND LUDTKE, O. 2016. Test-taking engagement in PIAAC. Tech. Rep. 133, Organisation for Economic Cooperation and Development, Paris.
GUNTHER, C. W. AND VANDERAALST, W. M. P. 2007. Fuzzy mining-adaptive process simplification based on multi-perspective metrics. In International Conference on Business Process Management. Springer, 328-343.
GUO, H., RIOS, J. A., HABERMAN, S., LIU, O. L., WANG, J., AND PAEK, I. 2016. A new procedure for detection of students' rapid guessing responses using response time. Applied Measurement in Education 29, 3, 173-183.
HOLSTEIN, K., MCLAREN, B. M., AND ALEVEN, V. 2018. Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In Proceedings of the 19th International Conference on Artificial Intelligence in Education, C. P. Rose, R. M. Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. M. McLaren, and B. du Boulay, Eds. Lecture Notes in Computer Science, vol. 10947. Springer, 154-168.
KONG, X. J., WISE, S. L., AND BHOLA, D. S. 2007. Setting the response time threshold parameter to differentiate solution behavior from rapid-guessing behavior. Educational and Psychological Measurement 67, 4, 606-619.
KUHN, M. AND JOHNSON, K. 2013a. Applied Predictive Modeling. Springer, New York, NY.
KUHN, M. AND JOHNSON, K. 2013b. An introduction to feature selection. In Applied Predictive Modeling. Springer, New York, NY, 487-519.
LEE, Y.-H. AND JIA, Y. 2014. Using response time to investigate students' test-taking behaviors in a NAEP computer-based study. Large-scale Assessments in Education 2, 1, 1-24.
LYNCH, C. F., BARNES, T., XUE, L., AND GITINABARD, N. 2017. Graph-based educational data mining. In Proceedings of the 10th International Conference on Educational Data Mining, X. Hu, T. Barnes, A. Hershkovitz, and L. Paquette, Eds. International Educational Data Mining Society.
PATEL, N., SELLMAN, C., AND LOMAS, D. 2017. Mining frequent learning pathways from a large educational dataset. arXiv preprint arXiv:1705.11125.
PATEL, N., SHARMA, A., SELLMAN, C., AND LOMAS, D. 2018. Curriculum pacing: A new approach to discover instructional practices in classrooms. In Proceedings of the 14th International Conference on Intelligent Tutoring Systems, R. Nkambou, R. Azevedo, and J. Vassileva, Eds. Lecture Notes in Computer Science, vol. 10858. Springer, 345-351.
PATIKORN, M. AND HEFFERNAN, N. 2019. Dataset. https://sites.google.com/view/dataminingcompetition2019/dataset#h.pndPGLzSaEKFb.
PEDRO, M. O. C. Z. S., BAKER, R. S. J., 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, G. Biswas, S. Bull, J. Kay, and A. Mitrovic, Eds. Lecture Notes in Computer Science, vol. 6738. Springer, 304-311.
PEDRO, M. O. C. Z. S., RODRIGO, M. M. T., AND BAKER, R. S. J. 2011. The relationship between carelessness and affect in a Cognitive Tutor. In Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, S. K. D'Mello, A. C. Graesser, B. W. Schuller, and J. Martin, Eds. Lecture Notes in Computer Science, vol. 6974. Springer, 306-315.
ROWE, J. P., MCQUIGGAN, S. W., ROBISON, J. L., AND LESTER, J. C. 2009. Off-task behavior in narrative-centered learning environments. In Proceedings of the 14th International Conference on Artificial Intelligence in Education, V. Dimitrova, R. Mizoguchi, B. du Boulay, and A. C. Graesser, Eds. Frontiers in Artificial Intelligence and Applications, vol. 200. IOSPress, 99-106.
SAHIN, F. 2019. Exploring the relations between students' time management strategies and test performance. Paper presented at the Annual meeting of the National Council for Measurement in Education.
SAHIN, F. AND COLVIN, K. F. 2020. Enhancing response time thresholds with response behaviors for detecting disengaged examinees. Large-scale Assessments in Education 8, 5, 1-24.
SLATTOW, G. 1977. Demonstration of the PLATO IV computer-based education system. Final report. January 1, 1972-June 30, 1976. Tech. rep., University of Illinois.
TRCKA, N., PECHENIZKIY, M., AND VANDERAALST, W. 2010. Process mining from educational data. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, Boca Raton, FL, Chapter 9, 123-142.
WALONOSKI, J. A. AND HEFFERNAN, N. T. 2006. Detection and analysis of off-task gaming behavior in intelligent tutoring systems. In Intelligent Tutoring Systems, M. Ikeda, K. Ashley, and T.-W. Chan, Eds. Lecture Notes in Computer Science, vol. 4053. Springer Berlin Heidelberg, 382-391.
WIXON, M., BAKER, R. S. J., GOBERT, J. D., OCUMPAUGH, J., AND BACHMANN, M. 2012. WTF? Detecting students who are conducting inquiry without thinking fastidiously. In Proceedings of the 20th International Conference on User Modeling, Adaptation, and Personalization, J. Masthoff, B. Mobasher, M. C. Desmarais, and R. Nkambou, Eds. Lecture Notes in Computer Science, vol. 7379. Springer, 286-296.
ZHOU, M., XU, Y., NESBIT, J. C., AND WINNE, P. H. 2010. Sequential pattern analysis of learning logs: Methodology and applications. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, Boca Raton, FL, Chapter 8, 107-121.
Section
Scientific Findings from the NAEP 2019 Data Mining Competition
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