Cognitive Tutors Produce Adaptive Online Course: Inaugural Field Trial

  • Noboru Matsuda
  • Martin van Velsen
  • Nikolaos Barbalios
  • Shuqiong Lin
  • Hardik Vasa
  • Roya Hosseini
  • Klaus Sutner
  • Norman Bier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)


We hypothesize that when cognitive tutors are integrated into online courseware, the online courseware can provide a new type of adaptive instructions, such as impasse-driven adaptive remediation and need-based assessments. As a proof of concept, we have developed an adaptive online course on the Open Learning Initiative (OLI) platform by integrating four new instances of cognitive tutors into an existing OLI course. Cognitive tutors were created with an innovative cognitive tutor authoring system called Watson. To evaluate the effectiveness of the adaptive online course, a quasi-experiment was conducted in a gateway course at Carnegie Mellon University. The results show that the proposed adaptive online course technology is robust enough to be used in actual classroom with mixed effect for learning.


Adaptive online course Active learning Cognitive tutors Authoring by demonstration SimStudent 



The research reported here was supported by National Science Foundation Award No. DRL-1418244.


  1. 1.
    Reid, P.: Categories for barriers to adoption of instructional technologies. Educ. Inf. Technol. 19(2), 383–407 (2012)CrossRefGoogle Scholar
  2. 2.
    Bascow, L.S., et al.: Barriers to Adoption of Online Learning Systems in U.S. Higher Education. ITHAKA S+R, New York (2012)Google Scholar
  3. 3.
    Griffiths, R., et al.: Interactive Online Learning on Campus: Testing MOOCs and Other Platforms in Hybrid Formats in the University System of Maryland. ITHAKA S+R, New York (2014)Google Scholar
  4. 4.
    Parthasarathy, M., Smith, M.A.: Valuing the institution: an expanded list of factors influencing faculty adoption of online education. Online J. Distance Learn. Adm. 12(2), 9 (2009)Google Scholar
  5. 5.
    Thille, C., Smith, J.: The Open Learning Initiative: Cognitively Informed E-Learning. The Observatory on Borderless Higher Education, London (2004)Google Scholar
  6. 6.
    Gannon-Cook, R., et al.: Motivators and inhibitors for university faculty in distance and e-learning. Br. J. Educ. Technol. 40(1), 149–163 (2009)CrossRefGoogle Scholar
  7. 7.
    Koedinger, K.R., Corbett, A.T., Perfetti, C.: The knowledge-learning-instruction framework: bridging the science-practice chasm to enhance robust student learning. Cogn. Sci. 36, 757–798 (2012)CrossRefGoogle Scholar
  8. 8.
    Bier, N., Strader, R., Zimmaro, D.: An approach to skill mapping in online courses. In: Learning with MOOCs, Cambridge, MA (2014)Google Scholar
  9. 9.
    EGA: Learning to Adapt: A Case for Accelerating Adaptive Learning in Higher Education. Education Growth Advisors, Boston (2013)Google Scholar
  10. 10.
    Ritter, S., et al.: Cognitive tutor: applied research in mathematics education. Psychon. Bull. Rev. 14(2), 249–255 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Corbett, A.T.: Cognitive computer tutors: solving the two-sigma problem. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds.) UM 2001. LNCS (LNAI), vol. 2109, pp. 137–147. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  12. 12.
    Beck, J.E., Gong, Y.: Wheel-spinning: students who fail to master a skill. In: Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 431–440. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Matsuda, N., Chandrasekaran, S., Stamper, J.: How quickly can wheel spinning be detected? In: Proceedings of the International Conference on Educational Data Mining (under review)Google Scholar
  14. 14.
    Matsuda, N., Cohen, W.W., Koedinger, K.R.: Teaching the teacher: tutoring SimStudent leads to more effective cognitive tutor authoring. Int. J. Artif. Intell. Educ. 25, 1–34 (2015)CrossRefGoogle Scholar
  15. 15.
    Muggleton, S.: Inductive logic programming. New Gener. Comput. 8(4), 295–318 (1991)CrossRefMATHGoogle Scholar
  16. 16.
    Koedinger, K.R., Mathan, S.: Distinguishing qualitatively different kinds of learning using log files and learning curves. In: Working Notes of the ITS 2004 Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Noboru Matsuda
    • 1
  • Martin van Velsen
    • 2
  • Nikolaos Barbalios
    • 1
  • Shuqiong Lin
    • 1
  • Hardik Vasa
    • 3
  • Roya Hosseini
    • 4
  • Klaus Sutner
    • 2
  • Norman Bier
    • 5
  1. 1.College of Education and Human DevelopmentTexas A&M UniversityCollege StationUSA
  2. 2.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  3. 3.School of Information SciencesUniversity of PittsburghPittsburghUSA
  4. 4.Intelligent Systems ProgramUniversity of PittsburghPittsburghUSA
  5. 5.Open Learning InitiativeCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations