Active Learners: Redesigning an Intelligent Tutoring System to Support Self-regulated Learning

  • Yanjin Long
  • Vincent Aleven
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8095)


Supporting students’ self-regulated learning (SRL) is an important topic in the learning sciences. Two critical processes involved in SRL are self-assessment and study choice. Intelligent tutoring systems (ITSs) have been shown to be effective in supporting students’ domain-level learning through guided problem-solving practice, but it is an open question how they can support SRL processes effectively, while maintaining or even enhancing their effectiveness at the domain level. We used a combination of user-centered design techniques and experimental classroom research to redesign and evaluate an ITS for linear equation solving so it supports self-assessment and study choice. We added three features to the tutor’ Open Learner Model (OLM) that may scaffold students’ self-assessment (self-assessment prompts, delaying the update of students’ progress bars, and providing progress information on the problem type level). We also designed a problem selection screen with shared student/system control and game-like features. We went through two iterations of design and conducted two controlled experiments with 160 local middle school students to evaluate the effectiveness of the new features. The evaluations reveal that the new OLM with self-assessment support facilitates students’ learning processes, and enhances their learning outcomes significantly. However, we did not find significant learning gains due to the problem selection feature. This work informs the design of future ITS that supports SRL.


Self-assessment study choice intelligent tutoring system open learner model user-centered design classroom evaluations 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bull, S., Ahmad, N., Johnson, M., Johan, R., Mabbott, A., Kerly, A.: Adaptive Navigation Support, Learner Control and Open Learner Models. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 275–278. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Feyzi-behnagh, R., Khezri, Z., Azevedo, R.: An Investigation of Accuracy of Metacognitive Judgments during Learning with an Intelligent Multi-Agent Hypermedia Environment. The Annual Meeting of the Cognitive Science Society, 96–101 (2011)Google Scholar
  3. 3.
    Long, Y., Aleven, V.: Students’ Understanding of Their Student Model. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 179–186. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Long, Y., Aleven, V.: Supporting Students’ Self-Regulated Learning with an Open Learner Model in a Linear Equation Tutor. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 219–228. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Waalkens, M., Aleven, V., Taatgen, N.: Does Supporting Multiple Student Strategies Lead to Greater Learning and Motivation? Investigating a Source of Complexity in the Architecture of Intelligent Tutoring Systems. Computers & Education 60, 159–171 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yanjin Long
    • 1
  • Vincent Aleven
    • 1
  1. 1.Human Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations