Decision Tree Modeling of Wheel-Spinning and Productive Persistence in Skill Builders
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Published
Jun 30, 2018
Shimin Kai
Ma. Victoria Almeda
Ryan S. Baker
Cristina Heffernan
Neil Heffernan
Abstract
Research on non-cognitive factors has shown that persistence in the face of challenges plays an important role in learning. However, recent work on wheel-spinning, a type of unproductive persistence where students spend too much time struggling without achieving mastery of skills, show that not all persistence is uniformly beneficial for learning. For this reason, it becomes increasingly pertinent to identify the key differences between unproductive and productive persistence toward informing interventions in computer-based learning environments. In this study, we use a classification model to distinguish between productive persistence and wheel-spinning in ASSISTments, an online math learning platform. Our results indicate that there are two types of students who wheel-spin: first, students who do not request any hints in at least one problem but request more than one bottom-out hint across any 8 problems in the problem set; second, students who never request two or more bottom out hints across any 8 problems, do not request any hints in at least one problem, but who engage in relatively short delays between solving problems of the same skill. These findings suggest that encouraging students to both engage in spaced practice and use bottom-out hints sparingly is likely helpful for reducing their wheel-spinning and improving learning. These findings also provide insight on when students are struggling and how to make students' persistence more productive.
How to Cite
Kai, S., Almeda, M. V., Baker, R. S., Heffernan, C., & Heffernan, N. (2018). Decision Tree Modeling of Wheel-Spinning and Productive Persistence in Skill Builders. Journal of Educational Data Mining, 10(1), 36–71. https://doi.org/10.5281/zenodo.3344810
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Keywords
predictive modeling, wheel-spinning, productive persistence, decision tree, intelligent tutoring system
References
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CHITEJI, N. (2010). Time-preference, non-cognitive skills and well-being across the life course: Do non-cognitive skills encourage healthy behavior? The American Economic Review, 100(2), 200.
CLONINGER, C. R., SVRAKIC, D. M., & PRZYBECK, T. R. (1993). A psychobiological model of temperament and character. Archives of General Psychiatry, 50(12), 975-990.
CEPEDA, N. J., PASHLER, H., VUL, E., WIXTED, J. T., & ROHRER, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380.
CREDÉ, M., TYNAN, M. C., & HARMS, P. D. (2017). Much Ado About Grit: A Meta-Analytic Synthesis of the Grit Literature. Journal of Personality and Social Psychology., 1113(3), 492-511.
DAVIS, J., & GOADRICH, M. (2006). The relationship between Precision-Recall and ROC curves. In W. Cohen & A. Moore (Eds.), Proceedings of the 23rd International Conference on Machine Learning (pp. 233-240). ACM.
DILLON, J. T. (1988). Questioning and Teaching: A Manual of Practice, New York Teachers College.
DE FRUYT, F., VAN DE WIELE, L., & VAN HEERINGEN, C. (2000). Cloninger's psychobiological model of temperament and character and the five-factor model of personality. Personality and Individual Differences, 29(3), 441-452.
DEKE, J. AND HAIMSON, J. (2006). Valuing student competencies: Which ones predict postsecondary educational attainment and earnings, and for whom? Final Report. Mathematica Policy Research, Inc.
DIGMAN, JOHN M. (1990). Personality structure: Emergence of the five-factor model. Annual review of psychology, 41(1), 417-440.
DILLON, J. T. (1988). Questioning and teaching: A manual of practice, New York Teachers College.
DUCKWORTH, A. L., PETERSON, C., MATTHEWS, M. D., & KELLY, D. R. (2007). Grit: perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087-1101.
DUCKWORTH, A. L., & QUINN, P. D. (2009). Development and validation of the Short Grit Scale (GRIT–S). Journal of Personality Assessment, 91(2), 166-174.
EISENBERGER, R., & LEONARD, J. M. (1980). Effects of conceptual task difficulty on generalized persistence. American Journal of Psychology, 93, 285–298.
FENG, M., ROSCHELLE, J., HEFFERNAN, N., FAIRMAN, J., & MURPHY, R. (2014). Implementation of an intelligent tutoring system for online homework support in an efficacy trial. In S. Trausan-Matu, K. Boyer, K. Crosby, & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (pp. 561-566). Springer International Publishing.
GARCIA, E. (2014). The need to address noncognitive skills in the education policy agenda. Briefing Paper 386. Economic Policy Institute.
GOLDBERG, L. R. (1990). An alternative “description of personality”: The Big-Five factor structure. Journal of Personality and Social Psychology, 59, 1216–1229.
HANLEY, J.A. AND MCNEIL, B.J. (1982) The meaning and use of the Area Under a Receiver Operating Characteristic (ROC) Curve. Radiology, 143, 29-36.
HECKMAN, J., HSEE, J. AND Y. RUBINSTEIN (2001). The GED is a mixed signal: The effect of cognitive and noncognitive skills on human capital and labor market. Working Paper, University of Chicago.
HEFFERNAN, N., HEFFERNAN, C., DIETZ, K., SOFFER, D., PELLEGRINO, J. W., GOLDMAN, S. R., & DAILEY, M. (2012). Improving mathematical learning outcomes through automatic reassessment and relearning. In Annual Meeting of American Educational Research Association, Vancouver, British Columbia.
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.
HUANG, X., CRAIG, S. D., XIE, J., GRAESSER, A., & HU, X. (2016). Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences, 47, 258-265.
KIM, Y. J., ALMOND, R. G., & SHUTE, V. J. (2016). Applying evidence-centered design for the development of game-based assessments in physics playground. International Journal of Testing, 16(2), 142-163.
KLEIN, R., SPADY, R., & WEISS, A. (1991). Factors affecting the output and quit propensities of production workers. The Review of Economic Studies, 58(5), 929-953.
KULIK, J. A., & FLETCHER, J. D. (2016). Effectiveness of intelligent tutoring systems: a metaanalytic review. Review of Educational Research, 86(1), 42-78.
LAURSEN, E. K. (2015). The power of grit, perseverance, and tenacity. Reclaiming Children and Youth, 23(4), 19-24.
JOHN, O. P., & SRIVASTAVA, S. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research, 2(1999), 102- 138.
MA, W., ADESOPE, O. O., NESBIT, J. C., & LIU, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901-918.
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