Utilizing the Generalized Intelligent Framework for Tutoring to Encourage Self-Regulated Learning

  • Anne M. Sinatra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8534)


Self-regulated learning is of particular importance to computer-based and online instruction, as students need to manage their own time and their interactions with the system. Elements of self-regulatory learning traditionally include the metacognitive strategies of the students (e.g., their knowledge of their planning, and assessment of their own progress), their management of educational goals (e.g., what information is most important to them, and should receive their primary attention), and the strategies that students use in order to study and retain the provided information [1, 2]. By incorporating feedback and guidance within computer-based learning activities it can encourage students to engage in successful self-regulated learning with a better awareness of their own cognition, and strategies. Intelligent tutoring systems can utilize adaptive scaffolding and guidance in order to support self-regulated student learning [3]. The Generalized Intelligent Framework for Tutoring (GIFT) [4] is an open-source adaptive tutoring system framework. The included tools within GIFT can be used to structure courses which guide individuals through the learning environment and are consistent with self-regulated best practices. The current paper includes a brief review of research into self-regulated learning in the context of computer-based and adaptive instruction. Further, the authoring capabilities of GIFT are discussed, and recommendations are given for future feature additions to GIFT which will benefit instructors who wish to develop courses that facilitate self-regulated learning.


Strategy assessment and individual differences Self-Regulated Learning Adaptive Tutoring Intelligent Tutoring Systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pintrich, P.R., DeGroot, E.V.: Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology 82(1), 33–40 (1990)CrossRefGoogle Scholar
  2. 2.
    Zimmerman, B.J.: Self-regulated learning and academic achievement: An overview. Educational Psychologist 25(1), 3–17 (1990)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Lajoie, S.P.: Extending the scaffolding metaphor. Instructional Science 33, 541–557 (2005)CrossRefGoogle Scholar
  4. 4.
    Sottilare, R.A., Brawner, K.W., Goldberg, B.S., Holden, H.K.: The Generalized Intelligent Framework for Tutoring (GIFT). Army Research Laboratory (2012)Google Scholar
  5. 5.
    Azevedo, R., Hadwin, A.F.: Scaffolding self-regulated learning and metacognition – Implications for the design of computer-based scaffolds. Instructional Science 33, 367–379 (2005)CrossRefGoogle Scholar
  6. 6.
    Winters, F.I., Greene, J.A., Costich, C.M.: Self-regulation of learning within computer-based learning environments: A critical analysis. Educational Psychology Review 20, 429–444 (2008)CrossRefGoogle Scholar
  7. 7.
    Azevedo, R., Cromley, J.G., Seibert, D.: Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemporary Educational Psychology 29, 344–370 (2004)CrossRefGoogle Scholar
  8. 8.
    Dabbagh, N.: Scaffolding: An important teacher competency in online learning. Tech.Trends 47(2), 39–44 (2003)CrossRefGoogle Scholar
  9. 9.
    Wood, D., Bruner, J., Ross, G.: The role of tutoring in problem solving. Journal of Child Psychology & Psychiatry & Allied Disciplines 17(2), 89–102 (1976)CrossRefGoogle Scholar
  10. 10.
    Puntambekar, S., Hubscher, R.: Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed? Educational Psychologist 40(1), 1–12 (2005)CrossRefGoogle Scholar
  11. 11.
    El Saadawi, G., Azevedo, R., Castine, M., Payne, V., Medvedeva, O., Tseytlin, E., et al.: Factors affecting feeling-of-knowing in a medical intelligent tutoring system: the role of immediate feedback as a metacognitive scaffold. Advances in Health Science Education 15, 9–30 (2010)CrossRefGoogle Scholar
  12. 12.
    Holden, H.K., Sinatra, A.M.: A guide to scaffolding and guided instruction. In: Sottilare, R., Graesser, A., Hu, X., Goldberg, B. (eds.) Design Recommendations for Intelligent Tutoring Systems. Instructional Strategies, vol. 2 (in press, 2014)Google Scholar
  13. 13.
    Zhang, M., Quintana, C.: Scaffolding strategies for supporting middle school students’ online inquiry processes. Computers & Education 58, 181–196 (2012)CrossRefGoogle Scholar
  14. 14.
    Yelland, N., Masters, J.: Rethinking scaffolding in the information age. Computers & Education 48, 362–382 (2007)CrossRefGoogle Scholar
  15. 15.
    Sottilare, R.A., Holden, H.K.: Motivations for a Generalized Intelligent Framework for Tutoring (GIFT) for authoring, instruction and analysis. In: AIED 2013 Workshops Proceedings, vol. 7, pp. 1–9 (2013)Google Scholar
  16. 16.
    Sottilare, R.A., Graesser, A.C., Hu, X., Holden, H.: Preface. In: Design Recommendations for Intelligent Tutoring Systems, vol. 1, pp. ii–xiii (2013)Google Scholar
  17. 17.
    Graesser, A.C., Wiemer-Hastings, K., Wiemer-Hastings, P., Kreuz, R.: AutoTutor: A simulation of a human tutor. Cognitive Systems Research 1(1), 35–51 (1999)CrossRefGoogle Scholar
  18. 18.
    Klein, G., Baxter, H.C.: Cognitive transformation theory: Contrasting cognitive and behavioral learning. In: The Interservice/Industry Training, Simulation & Education Conference (I/ITSEC), vol. 2006(1). National Training Systems Association (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anne M. Sinatra
    • 1
  1. 1.U.S. Army Research LaboratoryUSA

Personalised recommendations