Mastery-Oriented Shared Student/System Control Over Problem Selection in a Linear Equation Tutor

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)


Making effective problem selection decisions is a challenging Self-Regulated Learning skill. Students need to learn effective problem-selection strategies but also develop the motivation to use them. A mastery-approach orientation is generally associated with positive problem selection behaviors such as willingness to work on new materials. We conducted a classroom experiment with 200 6th – 8th graders to investigate the effectiveness of shared control over problem selection with mastery-oriented features (i.e., features that aim at fostering a mastery-approach orientation that simulates effective problem-selection behaviors) on students’ domain-level learning outcomes, problem-selection skills, enjoyment, future learning and future problem selection. The results show that shared control over problem selection accompanied by mastery-oriented features leads to significantly better learning outcomes, as compared to fully system-controlled problem selection, as well as better declarative knowledge of a key problem-selection strategy. Nevertheless, there was no effect on future problem selection and future learning. Our experiment contributes to prior literature by demonstrating that with tutor features to foster a mastery-approach orientation, shared control over problem selection can lead to significantly better learning outcomes than full system control.


Mastery-approach orientation Problem selection Self-Regulated Learning Learner control Classroom experiment Intelligent Tutoring System 


  1. 1.
    Aleven, V., McLaren, B.M., Roll, I., Koedinger, K.R.: Help helps, but only so much: research on help seeking with intelligent tutoring systems. Int. J. Artif. Intell. Educ. 26(1), 1–9 (2016)CrossRefGoogle Scholar
  2. 2.
    Atkinson, R.C.: Optimizing the learning of a second-language vocabulary. J. Exp. Psychol. 96(1), 124–129 (1972)CrossRefGoogle Scholar
  3. 3.
    Azevedo, R., Witherspoon, A., Chauncey, A., Burkett, C., Fike, A.: MetaTutor: a meta-cognitive tool for enhancing self-regulated learning. In: Proceedings of the AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems, pp. 14–19 (2009)Google Scholar
  4. 4.
    Clark, C.R., Mayer, E.R.: E-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. Jossey-Bass, San Francisco (2011)CrossRefGoogle Scholar
  5. 5.
    Corbett, A.: Cognitive mastery learning in the ACT programming tutor. AAAI Technical report, SS-00-01 (2000)Google Scholar
  6. 6.
    Corbalan, G., Kester, L., Van Merriënboer, J.J.G.: Selecting learning tasks: effects of adaptation and shared control on efficiency and task involvement. Contemp. Educ. Psychol. 33(4), 733–756 (2008)CrossRefGoogle Scholar
  7. 7.
    Long, Y., Aleven, V.: Active learners: redesigning an intelligent tutoring system to support self-regulated learning. In: Proceedings of the 8th European Conference on Technology Enhanced Learning, pp. 490–495 (2013)Google Scholar
  8. 8.
    Long, Y., Aman, Z., Aleven, V.: Motivational design in an intelligent tutoring system that helps students make good task selection decisions. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds.) AIED 2015. LNCS, vol. 9112, pp. 226–236. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  9. 9.
    Metcalfe, J.: Metacognitive judgments and control of study. Curr. Dir. Psychol. Sci. 18(3), 159–163 (2009)CrossRefGoogle Scholar
  10. 10.
    Niemiec, R.P., Sikorski, C., Walberg, H.J.: Learner-control effects: a review of reviews and a meta-analysis. J. Educ. Comput. Res. 15(2), 157–174 (1996)CrossRefGoogle Scholar
  11. 11.
    Roll, I., Aleven, V., McLaren, B.M., Koedinger, K.R.: Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learn. Instr. 21(2), 267–280 (2011)CrossRefGoogle Scholar
  12. 12.
    Schunk, D.H., Pintrich, P.R., Meece, J.L.: Motivation in Education: Theory, Research, and Applications. Pearson/Merrill Prentice Hall, Upper Saddle River (2008)Google Scholar
  13. 13.
    VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46(4), 197–221 (2011)CrossRefGoogle Scholar
  14. 14.
    Wolters, C.A., Yu, S.L., Pintrich, P.R.: The relation between goal orientation and students’ motivational beliefs and self-regulated learning. Learn. Individ. Differ. 8(3), 211–238 (1996)CrossRefGoogle Scholar
  15. 15.
    Zimmerman, B.J.: Attaining self-regulation: a social cognitive perspective. In: Boekaerts, M., Pintrich, P., Zeidner, M. (eds.) Handbook of Self-Regulation, pp. 1–39. Academic Press, San Diego (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Learning Research and Development Center, University of PittsburghPittsburghUSA
  2. 2.Human-Computer Interaction Institute, Carnegie Mellon UniversityPittsburghUSA

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