Abstract
Research in learning analytics and educational data mining has sometimes failed to distinguish between wheel-spinning and more productive forms of persistence, when students are working in online learning system. This work has, in cases, treated any student who completes more than ten items on a topic without mastering it as being in need of intervention. By contrast, the broader fields of education and human development have recognized the value of grit and persistence for long-term outcomes. In this paper, we compare the longitudinal impact of wheel-spinning and productive persistence (completing many items but eventually mastering the topic) in online learning, utilizing a publicly available data set. We connect behavior during learning in middle school mathematics to a student’s eventual enrollment (or failure to enroll) in college. We find that productive persistence during middle school mathematics is associated with a higher probability of college enrollment, and that wheel-spinning during middle school mathematics is not statistically significantly associated with college enrollment in either direction. The findings around productive persistence remain statistically significant even when controlling for affect and disengaged behavior.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Almeda, M.V.Q., Baker, R.S.: Predicting student participation in STEM careers: the role of affect and engagement during middle school. J. Educ. Data Min. 12(2), 33–47 (2020)
Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (2012)
Beck, J., Rodrigo, M.M.T.: Understanding wheel spinning in the context of affective factors. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 162–167. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07221-0_20
Beck, J., Gong, Y.: Wheel-spinning: students who fail to master a skill. In: Chad Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 431–440. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_44
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57(1), 289–300 (1995)
Botelho, A.F., Varatharaj, A., Inwegen, E.G.V., Heffernan, N.T.: Refusing to try: characterizing early stopout on student assignments. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge (2019)
Botelho, A.F., Varatharaj, A., Patikron, T., Doherty, D., Adjei, S.A., Beck, J.E.: Developing early detectors of student attrition and wheel spinning using deep learning. IEEE Trans. Learn. Technol. 12(2), 158–170 (2019)
CCSS-MA: Common Core State Standards for Mathematics, Washington, DC (2010)
Credé, M., Tynan, M.C., Harms, P.D.: Much ado about grit: a meta-analytic synthesis of the grit literature. J. Pers. Soc. Psychol. 113(3), 492 (2017)
Dekker, G.W., Pechenizkiy, M., Vleeshouwers, J.M.: Predicting students drop out: a case study. In: Proceedings of the International Conference on Educational Data Mining (2009)
Duckworth, A.: Grit: The Power of Passion and Perseverance. Scribner, New York (2016)
Duckworth, A.L., Peterson, C., Matthews, M.D., Kelly, D.R.: Grit: perseverance and passion for long-term goals. J. Pers. Soc. Psychol. 92(6), 1087 (2007)
Flores, R.M., Rodrigo, M.M.T.: Wheel-spinning models in a novice programming context. J. Educ. Comput. Res. 58, 1101–1120 (2020). https://doi.org/10.1177/0735633120906063
Gardner, J., Brooks, C.: Student success prediction in MOOCs. User Model. User-Adap. Inter. 28(2), 127–203 (2018). https://doi.org/10.1007/s11257-018-9203-z
Gong, Y., Beck, J.E.: Towards detecting wheel-spinning: future failure in mastery learning. In: Proceedings of the Second ACM Conference on Learning@ Scale (2015)
Heffernan, N.T., Heffernan, C.L.: The ASSISTments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. Int. J. Artif. Intell. Educ. 24(4), 470–497 (2014). https://doi.org/10.1007/s40593-014-0024-x
Kai, S., Almeda, M.V., Baker, R.S., Heffernan, C., Heffernan, N.: Decision tree modeling of wheel-spinning and productive persistence in skill builders. J. Educ. Data Min. 10(1), 36–71 (2018)
Käser, T., Klingler, S., Gross, M.:. When to stop? Towards universal instructional policies. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (2016)
Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N.: Predicting MOOC dropout over weeks using machine learning methods. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs (2014)
Matsuda, N., Chandrasekaran, S., Stamper, J.C.: How quickly can wheel spinning be detected? In: Proceedings of the International Conference on Educational Data Mining (2016)
Milliron, M.D., Malcolm, L., Kil, D.: Insight and Action analytics: three case studies to consider. Res. Pract. Assess.ment 9, 70–89 (2014)
Ocumpaugh, J., Baker, R.S., Rodrigo, M.M.T.: Baker Rodrigo Ocumpaugh monitoring protocol (BROMP) 2.0 technical and training manual. Teachers College, Columbia University and Ateneo Laboratory for the Learning Sciences, New York, NY and Manila, Philippines (2015)
Ostrow, K., Donnelly, C., Adjei, S., Heffernan, N.: Improving student modeling through partial credit and problem difficulty. In: Proceedings of the Second ACM Conference on Learning@ Scale. ACM (2015)
Pardos, Z.A., Baker, R.S., San Pedro, M.O., Gowda, S.M., Gowda, S.M.: Affective states and state tests: investigating how affect and engagement during the school year predict end-of-year learning outcomes. J. Learn. Anal. 1(1), 107–128 (2014)
Patikorn, T., Baker, R.S., Heffernan, N.T.: ASSISTments longitudinal data mining competition special issue: a preface. J. Educ. Data Min. 12(2), i–xi (2020)
Razzaq, L., Heffernan, N.: Scaffolding vs. Hints in the Assistment System. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 635–644. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_63
San Pedro, M.O., Ocumpaugh, J., Baker, R.S., Heffernan, N.T.: Predicting STEM and Non-STEM college major enrollment from middle school interaction with mathematics educational software. In: Proceedings of the International Conference on Educational Data Mining (2014)
San Pedro, M.O.Z., Baker, R., Bowers, A., Heffernan, N.: 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 (2013)
Tinto, V.: Leaving College: Rethinking the Causes and Cures of Student Attrition. University of Chicago Press, Chicago (1987)
Van Inwegen, E.G., Adjei, S.A., Wang, Y., Heffernan, N.T.: Using partial credit and response history to model user knowledge. In: Proceedings of the International Conference on Educational Data Mining (2015).
Wang, Y., Baker, R.: Grit and intention: why do learners complete MOOCs? Int. Rev. Res. Open Distrib. Learn. 19(3) (2018)
Wang, Y., Heffernan, N.T., Beck, J.E.: Representing student performance with partial credit. In: Proceedings of the International Conference on Educational Data Mining (2010)
Whitehill, J., Williams, J., Lopez, G., Coleman, C., Reich, J.: Beyond prediction: first steps toward automatic intervention in MOOC student stopout. In: Proceedings of the International Conference on Educational Data Mining (2015)
Xiong, X., Li, S., Beck, J.E.: Will you get it right next week: predict delayed performance in enhanced ITS mastery cycle. In: Proceedings of the Florida Artificial Intelligence Research Symposium (2013)
Xiong, Y., Li, H., Kornhaber, M.L., Suen, H.K., Pursel, B., Goins, D.D.: Examining the relations among student motivation, engagement, and retention in a MOOC: A structural equation modeling approach. Glob. Educ. Rev. 2(3), 23–33 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Adjei, S.A., Baker, R.S., Bahel, V. (2021). Seven-Year Longitudinal Implications of Wheel Spinning and Productive Persistence. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12748. Springer, Cham. https://doi.org/10.1007/978-3-030-78292-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-78292-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78291-7
Online ISBN: 978-3-030-78292-4
eBook Packages: Computer ScienceComputer Science (R0)