Cognitive Computer Tutors: Solving the Two-Sigma Problem

  • Albert Corbett
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2109)


Individual human tutoring is the most effective and most expensive form of instruction. Students working with individual human tutors reach achievement levels as much as two standard deviations higher than students in conventional instruction (that is, 50% of tutored students score higher than 98% of the comparison group). Two early 20th-century innovations attempted to offer benefits of individualized instruction on a broader basis: (1) mechanized individualized feedback (via teaching machines and computers) and (2) mastery learning (individualized pacing of instruction). On average each of these innovations yields about a half standard deviation achievement effect. More recently, cognitive computer tutors have implemented these innovations in the context of a cognitive model of problem solving. This paper examines the achievement effect size of these two types of student-adapted instruction in a cognitive programming tutor. Results suggest that cognitive tutors have closed the gap with and arguably surpass human tutors.


Standard Deviation Intelligent Tutoring System Teaching Machine Model Trace Instructional Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bloom, B.S.: The 2_Sigma Problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher 13 (1984) 3–15Google Scholar
  2. 2.
    Cohen, P.A., Kulik, J.A., Kulik, C.C.: Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal 19 (1984) 237–248CrossRefGoogle Scholar
  3. 3.
    Pressey, S.L.: A simple apparatus which gives tests and scores-and teaches. School and Society 23 (1926) 373–376Google Scholar
  4. 4.
    Kulik, J.A.: Meta-analytic studies of findings on computer-based instruction. In E. Baker & H. O’Neil (Eds.) Technology assessment in education and training. Lawrence Erlbaum, Mahwah, NJ (1994) 9–33Google Scholar
  5. 5.
    Kulik, J.A., Bangert, R.L., Williams, G.W.: Effects of computer-based teaching on secondary school students. Journal of Educational Psychology 75 (1983) 19–26CrossRefGoogle Scholar
  6. 6.
    Kulik, C.C., Kulik, J.A.: Effectiveness of computer-based instruction: An updated analysis. Computers in Human Behavior 7 (1991) 75–94CrossRefGoogle Scholar
  7. 7.
    Liao, Y.: Effects of computer-assisted instruction on cognitive outcomes: A metaanalysis. Journ al of Research on Computing in Education 24 (1992) 367–380Google Scholar
  8. 8.
    Niemiec, R., Walberg, H.J.: Comparative effectiveness of computer-assisted instruction: A synthesis of reviews. Journal of Educational Computing Research 3 (1987) 19–37Google Scholar
  9. 9.
    Bloom, B.S.: Learning for mastery. In Evaluation Comment, 1. UCLA Center for the Study of Evaluation of Instructional Programs, Los Angeles, CA (1968)Google Scholar
  10. 10.
    Keller, F.S.: “Good-bye teacher...”. Journal of Applied Behavioral Analysis 1 (1968) 79–89CrossRefGoogle Scholar
  11. 11.
    Kulik, C.C, Kulik, J.A., Bangert-Drowns, R.L.: Effectiveness of mastery learning programs: A meta-analysis. Review of Educational Research 60 (1990) 265–299CrossRefGoogle Scholar
  12. 12.
    Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: Lessons learned. Journal of the Learning Sciences 4 (1995) 167–207CrossRefGoogle Scholar
  13. 13.
    Corbett, A.T., Anderson, J.R.: Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. Proceedings of ACTM CHI’2001 Conference on Human Factors in Computing Systems (in press)Google Scholar
  14. 14.
    Corbett, A.T., Anderson, J.R.: Knowledge decomposition and subgoal reification in the ACT Programming Tutor. Artificial Intelligence and Education, 1995: The Proceedings of AI-ED 95. AACE., Charlottesville, VA (1995) 469–476Google Scholar
  15. 15.
    Corbett, A.T., Knapp, S.: Plan scaffolding: Impact on the process and product of learning. In C. Frasson, G. Gauthier, & A. Lesgold (Eds.) Intelligent tutoring systems: Third international conference, ITS’ 96. Springer, New York (1996) 120–129Google Scholar
  16. 16.
    Corbett, A.T., Bhatnagar, A.: Student modeling in the ACT Programming Tutor: Adjusting a procedural learning model with declarative knowledge. User Modeling: Proceedings of the Sixth International Conference, UM97. Springer, New York, (1997) 243–254Google Scholar
  17. 17.
    Corbett, A.T., Trask, H.: Instructional interventions in computer-based tutoring: Differential impact on learning time and accuracy. Proceedings of ACTM CHI’2000 Conference on Human Factors in Computing Systems. Springer, New York (2000) 97–104CrossRefGoogle Scholar
  18. 18.
    Anderson, J.R., Gluck, K.: What role do cognitive architectures play in intelligent tutoring systems. In D. Klahr & S. Carver (Eds.) Cognition and instruction: 25 years of progress. Lawrence Erlbaum, Mahwah, NJ (in press)Google Scholar
  19. 19.
    Aleven, V., Koedinger, K.R.: Toward a tutorial dialog system that helps students to explain solution steps. Building Dialogue Systems for Tutorial Applications: AAAI Fall Symposium 2000, (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Albert Corbett
    • 1
  1. 1.Human-Computer Interaction Institute Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations