Abstract
Understanding how students allocate their time to different learning behaviors, especially those that distinguish students’ performances, can yield significant implications for the design of intelligent tutoring systems (ITS). Time on task is a typical indicator of students’ self-regulated learning (SRL) and student engagement. In this paper, we analyze log file data to identify patterns in the behavior durations of 62 medical students in BioWorld, an ITS that supports them in regulating their diagnostic reasoning while solving complex patient cases. Results demonstrated that task complexity mediated the relationship between students’ allocation of time and diagnostic performance outcomes. The high-performing students showed different patterns of time management with low-performing students when solving both simple and complex cases. Moreover, the durations of behaviors predicted students’ performance in clinical reasoning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
van Ewijk, C.D.: Assessing students’ acquisition of self-regulated learning skills using meta-analysis. In: Azevedo, R., Aleven, V. (eds.) Handbook of Self-Regulation of Learning and Performance, pp. 376–390. Routledge, Abingdon (2011)
Taub, M., Azevedo, R., Bradbury, A.E., Millar, G.C., Lester, J.: Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment. Learn. Instr. 54, 93–103 (2017)
Zimmerman, B.J., Schunk, D.H.: Self-regulated learning and performance: an introduction and an overview. In: Handbook of Self-Regulation of Learning and Performance, pp. 1–12 (2011)
Poitras, E.G., Lajoie, S.P.: A domain-specific account of self-regulated learning: the cognitive and metacognitive activities involved in learning through historical inquiry. Metacogn. Learn. 8, 213–234 (2013). https://doi.org/10.1007/s11409-013-9104-9
Biswas, G., Segedy, J.R., Bunchongchit, K.: From design to implementation to practice a learning by teaching system: Betty’s brain. Int. J. Artif. Intell. Educ. 26, 350–364 (2016). https://doi.org/10.1007/s40593-015-0057-9
Lajoie, S.P., Naismith, L., Poitras, E., Hong, Y.-J., Cruz-Panesso, I., Ranellucci, J., Mamane, S., Wiseman, J.: Technology-rich tools to support self-regulated learning and performance in medicine. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies. SIHE, vol. 28, pp. 229–242. Springer, New York (2013). https://doi.org/10.1007/978-1-4419-5546-3_16
Azevedo, R., Johnson, A., Chauncey, A., Burkett, C.: Self-regulated learning with metatutor: advancing the science of learning with metacognitive tools. In: Khine, M., Saleh, I. (eds.) New Science of Learning: Cognition, Computers and Collaboration in Education, pp. 225–247. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-5716-0_11
Li, S., Zheng, J.: The effect of academic motivation on students’ English learning achievement in the eSchoolbag-based learning environment. Smart Learn. Environ. 4, 3 (2017). https://doi.org/10.1186/s40561-017-0042-x
Zimmerman, B.J.: Becoming a self-regulated learner: an overview. Theory Pract. 41, 64–70 (2002). https://doi.org/10.1207/s15430421tip4102_2
Roll, I., Winne, P.H.: Understanding, evaluating, and supporting self-regulated learning using learning analytics. J. Learn. Anal. 2, 7–12 (2015). https://doi.org/10.18608/jla.2015.21.2
Lajoie, S.P.: Developing professional expertise with a cognitive apprenticeship model: examples from avionics and medicine. In: Ericsson, K.A. (ed.) Development of Professional Expertise: Toward Measurement of Expert Performance and Design of Optimal Learning Environments, pp. 61–83. Cambridge University Press, New York (2009)
Pintrich, P.R.: The role of goal orientation in self-regulated learning. Handb. Self-Regul. 451–502 (2000). https://doi.org/10.1016/b978-012109890-2/50043-3
Bouchet, F., Azevedo, R., Kinnebrew, J.S., Biswas, G.: Identifying students’ characteristic learning behaviors in an intelligent tutoring system fostering self-regulated learning. In: Proceedings of the 5th International Conference on Education Data Mining (EDM 2012), pp. 65–72 (2012)
Hadwin, A.F., Nesbit, J.C., Jamieson-Noel, D., Code, J., Winne, P.H.: Examining trace data to explore self-regulated learning. Metacogn. Learn. 2, 107–124 (2007). https://doi.org/10.1007/s11409-007-9016-7
Kostons, D., van Gog, T., Paas, F.: Training self-assessment and task-selection skills: a cognitive approach to improving self-regulated learning. Learn. Instr. 22, 121–132 (2012). https://doi.org/10.1016/j.learninstruc.2011.08.004
Bannert, M., Reimann, P., Sonnenberg, C.: Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacogn. Learn. 9, 161–185 (2014). https://doi.org/10.1007/s11409-013-9107-6
Poitras, E.G., Doleck, T., Lajoie, S.P.: Towards detection of learner misconceptions in a medical learning environment: a subgroup discovery approach. Educ. Technol. Res. Dev. 66, 129–145 (2017)
Ericsson, K.A., Charness, N.: Expert performance: its structure and acquisition. Am. Psychol. 49, 725–747 (1994). https://doi.org/10.1037/0003-066X.49.8.725
Lazakidou, G., Retalis, S.: Using computer supported collaborative learning strategies for helping students acquire self-regulated problem-solving skills in mathematics. Comput. Educ. 54, 3–13 (2010). https://doi.org/10.1016/j.compedu.2009.02.020
Narciss, S., Proske, A., Koerndle, H.: Promoting self-regulated learning in web-based learning environments. Comput. Hum. Behav. 23, 1126–1144 (2007). https://doi.org/10.1016/j.chb.2006.10.006
Vighnarajah, Wong, S.L., Abu Bakar, K.: Qualitative findings of students’ perception on practice of self-regulated strategies in online community discussion. Comput. Educ. 53, 94–103 (2009). https://doi.org/10.1016/j.compedu.2008.12.021
Zimmerman, B.J.: Self-regulated learning and academic achievement: an overview. Educ. Psychol. 25, 3–17 (2010). https://doi.org/10.1207/s15326985ep2501
R Core Team: R: A Language and Environment for Statistical Computing (2014)
Hothorn, T., Hornik, K., Zeileis, A.: Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15, 651–674 (2006). https://doi.org/10.1198/106186006X133933
Hothorn, T., Hornik, K., Strobl, C., Zeileis, A.: Party: a laboratory for recursive partytioning. R Package version 0.9-0. 37 (2006). 10.1.1.151.2872
Strobl, C., Boulesteix, A.L., Zeileis, A., Hothorn, T.: Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform. 8, 25 (2007). https://doi.org/10.1186/1471-2105-8-25
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Li, S., Zheng, J., Poitras, E., Lajoie, S. (2018). The Allocation of Time Matters to Students’ Performance in Clinical Reasoning. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science(), vol 10858. Springer, Cham. https://doi.org/10.1007/978-3-319-91464-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-91464-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91463-3
Online ISBN: 978-3-319-91464-0
eBook Packages: Computer ScienceComputer Science (R0)