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

  • Yanjin LongEmail author
  • Vincent Aleven
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 



We thank Gail Kusbit, Jonathan Sewall, Octav Popescu and Mike Stayton for their kind help with the classroom experiment. We also thank the participating teachers and students. This work is funded by an NSF grant to the Pittsburgh Science of Learning Center (NSF Award SBE0354420).


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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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