Advertisement

Help Seeking and Intelligent Tutoring Systems: Theoretical Perspectives and a Step Towards Theoretical Integration

  • Vincent Aleven
Chapter
Part of the Springer International Handbooks of Education book series (SIHE, volume 28)

Abstract

Help seeking is a strategy highlighted in a number of theories of self-­regulated learning (SRL). We focus on the help-seeking behavior of students during tutored problem solving with an intelligent tutoring system (ITS), specifically, the Geometry Cognitive Tutor. ITSs are an advanced type of computer-based learning environment (CBLE) and are in widespread use. These systems typically provide step-by-step guidance with complex problems, including on-demand help. A number of theories shed light on how on-demand help focused on problem-solving principles can help students acquire robust knowledge (i.e., knowledge that transfers to novel situations, lasts over time, and may facilitate future learning), but they also highlight challenges students face in doing so. These theories include the ACTR theory of cognition and learning, the Knowledge-Learning-Instruction theoretical framework focused on learning from instruction, SRL theories, and educational psychology theories of help seeking. Given the variety of perspectives, we see a strong need for theoretical integration. As a modest first step, we review our own work on rule-based modeling of help seeking, which integrates cognitive and metacognitive aspects within a single modeling framework.

Keywords

Procedural Knowledge Declarative Knowledge Intelligent Tutoring System Instructional Explanation Metacognitive Monitoring 
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.

Notes

Acknowledgements

The writing of this chapter was supported by the Pittsburgh Science of Learning Center, which is funded by the National Science Foundation (# SBE 0836012). Roger Azevedo, Matt Bernacki, Albert Corbett, Yanjin Long, Timothy Nokes, and Charles Perfetti gave very helpful comments on earlier versions of the chapter. We gratefully acknowledge their contributions.

The writing of this chapter was sponsored by National Science Foundation award SBE0354420 to the Pittsburgh Science of Learning Center.

References

  1. Aleven, V. (2010). Rule-based cognitive modeling for intelligent tutoring systems. In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds.), Advances in intelligent tutoring systems (pp. 33–62). Berlin: Springer.CrossRefGoogle Scholar
  2. Aleven, V., & Koedinger, K. R. (2000). Limitations of student control: Do students know when they need help? In G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Proceedings of the 5th International Conference on Intelligent Tutoring Systems, ITS 2000 (pp. 292–303). Berlin: Springer.Google Scholar
  3. Aleven, V., & Koedinger, K. R. (2002). An effective meta-cognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2), 147–179.CrossRefGoogle Scholar
  4. Aleven, V., McLaren, B. M., & Koedinger, K. R. (2006). Towards computer-based tutoring of help-seeking skills. In S. Karabenick & R. Newman (Eds.), Help seeking in academic settings: Goals, groups, and contexts (pp. 259–296). Mahwah, NJ: Erlbaum.Google Scholar
  5. Aleven, V., McLaren, B., Roll, I., & Koedinger, K. (2006). Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence in Education, 16, 101–128.Google Scholar
  6. Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2010). Automated, unobtrusive, action-by-action assessment of self-regulation during learning with an intelligent tutoring system. Educational Psychologist, 45(4), 224–233.CrossRefGoogle Scholar
  7. Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. M. (2003). Help seeking and help design in interactive learning environments. Review of Educational Research, 73(3), 277–320.CrossRefGoogle Scholar
  8. Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.Google Scholar
  9. Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989). Skill acquisition and the Lisp Tutor. Cognitive Science, 13, 467–505.CrossRefGoogle Scholar
  10. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207.CrossRefGoogle Scholar
  11. Anderson, J. R., & Lebière, C. (1998). The atomic components of thought. Mahwah, NJ: Erlbaum.Google Scholar
  12. Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemporary Educational Psychology, 29, 344–370.CrossRefGoogle Scholar
  13. Azevedo, R., Cromley, J. G., Winters, F. I., Moos, D. C., & Greene, J. A. (2005). Adaptive human scaffolding facilitates adolescents’ self-regulated learning with hypermedia. Instructional Science, 33, 381–412. doi: 10.1007/s11251-005-1273-8.CrossRefGoogle Scholar
  14. Azevedo, R., Harley, J., Trevors, G., Feyzi-Behnagh, R., Duffy, M., & Bouchet, F. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo & V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies (pp. ). Springer International Handbooks of Education 26 New York: Springer. DOI:  10.1007/978-1-4419-5546-3_21.
  15. Azevedo, R., & Jacobson, M. J. (2008). Advances in scaffolding learning with hypertext and hypermedia: A summary and critical analysis. Educational Technology Research and Development, 56(1), 93–100.CrossRefGoogle Scholar
  16. Azevedo, R., Johnson, A. M., Chauncey, A., & Graesser, A. (2011). Use of hypermedia to assess and convey self-regulated learning. In B. Zimmerman & D. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 102–121). New York: Routledge.Google Scholar
  17. Azevedo, R., Moos, D. C., Johnson, A. M., & Chauncey, A. D. (2010). Measuring cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges. Educational Psychologist, 45(4), 210–223. doi: 10.1080/00461520.2010.515934.CrossRefGoogle Scholar
  18. Azevedo, R., & Witherspoon, A. M. (2009). Self-regulated learning with hypermedia. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 319–339). Mahwah, NJ: Routledge.Google Scholar
  19. Beal, C. R., Walles, R., Arroyo, I., & Woolf, B. P. (2007). Online tutoring for math achievement: A controlled evaluation. Journal of Interactive Online Learning, 6, 43–55.Google Scholar
  20. Beck, J. E., Chang, K., Mostow, J., & Corbett, A. T. (2008). Does help help? Introducing the bayesian evaluation and assessment methodology. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the 9th International Conference on Intelligent Tutoring Systems (pp. 383–394). Berlin: Springer.Google Scholar
  21. Boekaerts, M. (2007). Understanding Students’ affective processes in the classroom. In P. Schutz, R. Pekrun, & G. Phye (Eds.), Emotion in education (pp. 37–56). San Diego, CA: Academic Press.CrossRefGoogle Scholar
  22. Boekaerts, M., & Rozendaal, J. S. (2010). Using multiple calibration indices in order to capture the complex picture of what affects students’ accuracy of feeling of confidence. Leaning and Instruction, 20, 372–382.CrossRefGoogle Scholar
  23. Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. Washington: National Academic Press.Google Scholar
  24. Brown, A. (1987). Metacognition, executive control, self-regulation, and other mysterious mechanisms. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 65–116). Hillsdale, NJ: Erlbaum.Google Scholar
  25. Butcher, K. R., & Aleven, V. (2010). Learning during intelligent tutoring: When do integrated visual-verbal representations improve student outcomes? In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (pp. 2888–2893). Austin, TX: Cognitive Science Society.Google Scholar
  26. Butcher, K., & Aleven, V. (in press). Using student interactions to foster rule-diagram mapping during problem solving in an intelligent tutoring system. Journal of Educational Psychology.Google Scholar
  27. Campuzano, L., Dynarski, M., Agodini, R., & Rall, K. (2009). Effectiveness of reading and mathematics software products: Findings from two student cohorts. Washington, DC: U.S. Department of Education, Institute of Education Sciences.Google Scholar
  28. Card, S., Moran, T., & Newell, A. (1983). The psychology of human-computer interaction. Mahwah, NJ: Erlbaum.Google Scholar
  29. Chen, S. (2002). A cognitive model for non-linear learning in hypermedia programmes. British Journal of Educational Technology, 33(4), 449–460.CrossRefGoogle Scholar
  30. Chi, M. T. H. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology (pp. 161–237). Mahwah, NJ: Erlbaum.Google Scholar
  31. Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.CrossRefGoogle Scholar
  32. Chi, M. T. H., de Leeuw, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.Google Scholar
  33. Conati, C. (2013). Modeling and scaffolding self-explanation across domains and activities. In R. Azevedo & V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies (pp. ). Springer International Handbooks of Education 26 New York: Springer. DOI:  10.1007/978-1-4419-5546-3_21.
  34. Conati, C., & Vanlehn, K. (2000). Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education, 11(4), 389–415.Google Scholar
  35. Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253–278.CrossRefGoogle Scholar
  36. Corbett, A. T., & Anderson, J. R. (2001). Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. In J. Jacko, A. Sears, M. Beaudouin-Lafon, & R. Jacob (Eds.), Proceedings of ACM CHI2001 Conference on Human Factors in Computing Systems (pp. 245–252). New York: ACM Press.Google Scholar
  37. Corbett, A., Kauffman, L., MacLaren, B., Wagner, A., & Jones, E. (2010). A Cognitive Tutor for genetics problem solving: Learning gains and student modeling. Journal of Educational Computing Research, 42(2), 219–239.CrossRefGoogle Scholar
  38. Corbett, A., McLaughlin, M., & Scarpinatto, K. C. (2000). Modeling student knowledge: Cognitive Tutors in high school and college. User Modeling and User-Adapted Interaction, 10, 81–108.CrossRefGoogle Scholar
  39. Dunlosky, J., & Lipko, A. (2007). Metacomprehension: A brief history and how to improve its accuracy. Current Directions in Psychological Science, 16, 228–232.CrossRefGoogle Scholar
  40. Dunlosky, J., & Metcalfe, J. (2008). Metacognition. Thousand Oaks, CA: Sage.Google Scholar
  41. Feyzi-Behnagh, R., Khezri, Z., & Azevedo, R. (2011). An investigation of accuracy of metacognitive judgments during learning with an intelligent multi-agent hypermedia environment. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 96–101). Austin, TX: Cognitive Science Society.Google Scholar
  42. Flavell, J. (1979). Metacognition and cognitive monitoring. A new area of cognitive development inquiry. American Psychologist, 34, 906–911.CrossRefGoogle Scholar
  43. Glenberg, A. M., & Epstein, W. (1985). Calibration of comprehension. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11, 702–718.Google Scholar
  44. Goldin, I., Koedinger, K. R., & Aleven, V. (2012). Learner differences in hint processing. In K. Yacef, O. Zaïane, A. Hershkovitz, M. Yudelson, & J. Stamper (Eds.), Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012) (pp. 73–80). Worcester, MA: International Educational Data Mining Society.Google Scholar
  45. Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2, 107–124. doi: 10.1007/s11409-007-9016-7.CrossRefGoogle Scholar
  46. Hatano, G., & Inagaki, I. (1986). Two courses of expertise. In H. A. H. Stevenson & K. Hakuta (Eds.), Child development and education in Japan (pp. 262–272). New York: Freeman.Google Scholar
  47. Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger, & J. Greer (Eds.), Proceedings of the 13th International Conference on Artificial Intelligence in Education (pp. 417–424). Amsterdam: IOS Press.Google Scholar
  48. Jacobson, M. J. (2008). Hypermedia systems for problem-based learning: Theory, research, and learning emerging scientific conceptual perspectives. Educational Technology, Research, and Development, 56, 5–28.CrossRefGoogle Scholar
  49. Jacobson, M. J., & Archodidou, A. (2000). The design of hypermedia tools for learning: Fostering conceptual change and transfer of complex scientific knowledge. The Journal of the Learning Sciences, 9(2), 145–199.CrossRefGoogle Scholar
  50. Karabenick, S., & Newman, R. (Eds.). (2006). Help seeking in academic settings: Goals, groups, and contexts. Mahwah, NJ: Erlbaum.Google Scholar
  51. Kilpatrick, J., Swafford, J., & Findell, B. (Eds.). (2001). Adding it up: Helping children learn mathematics. Mathematics Learning Study Committee, Center for Education, Division of Behavioral and Social Sciences and Education. Washington, DC: Academy Press.Google Scholar
  52. Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19(3), 239–264.CrossRefGoogle Scholar
  53. Koedinger, K. R., Aleven, V., Roll, I., & Baker, R. (2009). In vivo experiments on whether supporting metacognition in intelligent tutoring systems yields robust learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 897–964). The Educational Psychology Series. New York: Routledge.Google Scholar
  54. Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30–43.Google Scholar
  55. Koedinger, K. R., Baker, R., Cunningham, K., Skogsholm, A., Leber, B., & Stamper, J. (2011). A data repository for the EDM community: The PSLC DataShop. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. D. Baker (Eds.), Handbook of educational data mining (pp. 43–55). Boca Raton, FL: CRC Press.Google Scholar
  56. Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning sciences to the classroom. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 61–78). New York: Cambridge University Press.Google Scholar
  57. Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757–798. doi: 10.1111/j.1551-6709.2012.01245.x.CrossRefGoogle Scholar
  58. Koedinger, K., Cunningham, K., Skogsholm, A., & Leber, B. (2008). An open repository and analysis tools for fine-grained, longitudinal learner data. In R. S. J. D. Baker, T. Barnes, & J. E. Beck (Eds.), Proceedings of the 1st International Conference on Educational Data Mining, EDM 2008 (pp. 157–166). Worcester, MA: International Educational Data Mining Society.Google Scholar
  59. Koriat, A., & Bjork, R. A. (2005). Illusions of competence in monitoring one’s knowledge during study. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 187–194.Google Scholar
  60. Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: The Betty’s Brain system. International Journal of Artificial Intelligence in Education, 18(3), 181–208.Google Scholar
  61. Long, Y., & Aleven, V. (2012). Skill diaries: Can periodic self-assessment improve students’ learning with an intelligent tutoring system? In S. A. Cerri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems, ITS 2012 (pp. 673–674). Berlin: Springer.Google Scholar
  62. Luckin, R., & Hammerton, L. (2002). Getting to know me: Helping learners understand their own learning needs through metacognitive scaffolding. In S. Cerri, G. Gouardères, & F. Paraguaçu (Eds.), Proceedings of the 6th International Conference on Intelligent Tutoring Systems, ITS 2002 (pp. 759–771). Berlin: Springer. doi:  10.1007/3-540-47987-2_76.Google Scholar
  63. Ma, L. (1999). Knowing and teaching elementary mathematics: Teachers’ understanding of fundamental mathematics in China and the United States. Mahwah, NJ: Erlbaum.Google Scholar
  64. Mathan, S. A., & Koedinger, K. R. (2005). Fostering the intelligent novice: Learning from errors with metacognitive tutoring. Educational Psychologist, 40(4), 257–265.CrossRefGoogle Scholar
  65. McLaren, B. M., DeLeeuw, K. E., & Mayer, R. E. (2011). Polite web-based intelligent tutors: Can they improve learning in classrooms? Computers & Education, 56(3), 574–584. doi: 10.1016/j.compedu.2010.09.019.CrossRefGoogle Scholar
  66. McNamara, D. S., & Magliano, J. P. (2009). Self-explanation and metacognition: The dynamics of reading. In D. J. Hacker, J. Dunlosky, & A. Graesser (Eds.), Handbook of metacognition in education (pp. 60–81). New York: Routledge/Taylor & Francis.Google Scholar
  67. Mitrovic, A., Martin, B., & Mayo, M. (2002). Using evaluation to shape ITS design: Results and experiences with SQL-Tutor. International Journal of User Modeling and User-Adapted Interaction, 12(2–3), 243–279.CrossRefGoogle Scholar
  68. Nelson, T. O. (1996). Consciousness and metacognition. American Psychologist, 51, 102–116.CrossRefGoogle Scholar
  69. Nelson-Le Gall, S. (1981). Help-seeking: An understudied problem-solving skill in children. Developmental Review, 1, 224–246.CrossRefGoogle Scholar
  70. Nelson-Le Gall, S. (1985). Help-seeking behavior in learning. Review of Research in Education, 12, 55–90.Google Scholar
  71. Nelson-Le Gall, S., Kratzer, L., Jones, E., & DeCooke, P. (1990). Children’s self-assessment of performance and task-related help-seeking. Journal of Experimental Child Psychology, 49, 245–263.CrossRefGoogle Scholar
  72. Newman, R. S. (1994). Adaptive help seeking: A strategy of self-regulated learning. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulation of learning and performance: Issues and educational applications (pp. 283–301). Hillsdale, NJ: Erlbaum.Google Scholar
  73. Newman, R. S. (1998). Adaptive help seeking: A role of social interaction in self-regulated learning. In S. A. Karabenick (Ed.), Strategic help seeking. Implications for learning and teaching (pp. 13–37). Mahwah: Erlbaum.Google Scholar
  74. Newman, R. S. (2008). The motivational role of adaptive help seeking in self-regulated learning. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 315–37). New York: Erlbaum.Google Scholar
  75. Newman, R. S., & Goldin, L. (1990). Children’s reluctance to seek help with schoolwork. Journal of Educational Psychology, 82, 92–100.CrossRefGoogle Scholar
  76. Nkambou, R., Bourdeau, J., & Mizoguchi, R. (Eds.). (2010). Advances in intelligent tutoring systems. Berlin: Springer.Google Scholar
  77. Otieno, C., Schwonke, R., Renkl, A., Aleven, V., & Salden, R. (2011). Measuring learning progress via self-explanations versus problem solving - a suggestion for optimizing adaptation in intelligent tutoring systems. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 84–89). Austin, TX: Cognitive Science Society.Google Scholar
  78. Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated learning. Educational Psychologist, 36(2), 89–101.CrossRefGoogle Scholar
  79. Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407.CrossRefGoogle Scholar
  80. Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MLSQ). Educational and Psychological Measurement, 53, 801–813.CrossRefGoogle Scholar
  81. Rawson, K. A., & Dunlosky, J. (2013). Retrieval-Monitoring-Feedback (RMF) technique for producing efficient and durable student learning. In R. Azevedo & V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies (pp. ). Springer International Handbooks of Education 26 New York: Springer. doi:  10.1007/978-1-4419-5546-3_21.
  82. Renkl, A. (1997). Learning from worked-out examples: a study on individual differences. Cognitive Science, 21, 1–29.CrossRefGoogle Scholar
  83. Renkl, A., Berthold, K., Grosse, C. S., & Schwonke, R. (2013). Making better use of multiple representations: How fostering metacognition can help. In R. Azevedo & V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies (pp.). Springer International Handbooks of Education 26 New York: Springer. DOI 10.1007/978-1-4419-5546-3_21.Google Scholar
  84. Renkl, A., Stark, R., Gruber, H., & Mandl, H. (1998). Learning from worked-out examples: The effects of example variability and elicited self-explanations. Contemporary Educational Psychology, 23, 90–108.CrossRefGoogle Scholar
  85. Ritter, S., Kulikowich, J., Lei, P., McGuire, C., & Morgan, P. (2007). What evidence matters? A randomized field trial of Cognitive Tutor® Algebra I. In T. Hirashima, H. U. Hoppe, & S. Shwu-Ching Young (Eds.), Supporting learning flow through integrative technologies (pp. 13–20). The Netherlands: IOS Press.Google Scholar
  86. Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93(2), 346–362.CrossRefGoogle Scholar
  87. Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2011). Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction, 21(2), 267–280.CrossRefGoogle Scholar
  88. Roll, I., Baker, R. S. J. d., Aleven, V., & Koedinger, K. R. (under review). The effect of overuse and underuse of help resources in intelligent tutoring systems. Manuscript submitted for publication.Google Scholar
  89. Ryan, A. M., Pintrich, P. R., & Midgley, C. (2001). Avoiding seeking help in the classroom: Who and why? Educational Psychology Review, 13(2), 93–114.CrossRefGoogle Scholar
  90. Salden, R., Aleven, V., Schwonke, R., & Renkl, A. (2010). The expertise reversal effect and worked examples in tutored problem solving: Benefits of adaptive instruction. Instructional Science, 38(3), 289–307. doi: 10.1007/s11251-009-9107-8.CrossRefGoogle Scholar
  91. Scheines, R., & Sieg, W. (1994). Computer environments for proof construction. Interactive Learning Environments, 4(2), 159–169.CrossRefGoogle Scholar
  92. Shih, B., Koedinger, K. R., & Scheines, R. (2008). A response time model for bottom-out hints as worked examples. In R. S. J. D. Baker, T. Barnes, & J. Beck (Eds.), Proceedings of the 1st International Conference on Educational Data Mining, EDM 2008 (pp. 117–26). Worcester, MA: International Educational Data Mining Society.Google Scholar
  93. Shih, B., Koedinger, K. R., & Scheines, R. (2010). Unsupervised discovery of student learning tactics. In R. S. J. D. Baker, A. Merceron, & P. I. Pavlik Jr. (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining, EDM 2010 (pp. 201–210). Worcester, MA: International Educational Data Mining Society.Google Scholar
  94. Simons, D. J., & Chabris, C. F. (2011). What people believe about how memory works: A representative survey of the U.S. population. PLoS One, 6(8), e22757. doi: 10.1371/journal.pone.0022757.CrossRefGoogle Scholar
  95. Stamper, J., Barnes, T., & Croy, M. (2012). Enhancing the automatic generation of hints with expert seeding. International Journal of Artificial Intelligence in Education, 21(2), 153–167.Google Scholar
  96. Stamper, J., Eagle, M., Barnes, T., & Croy, M. (2011). Experimental evaluation of automatic hint generation for a logic tutor. In J. Kay, S. Bull, & G. Biswas (Eds.), Proceeding of the 15th International Conference on Artificial Intelligence in Education (AIED2011) (pp. 345–352). Berlin: Springer.Google Scholar
  97. Stamper, J., Koedinger, K. R., Baker, R., Skogsholm, A., Leber, B., Demi, S., et al. (2011). Managing the educational dataset lifecycle with DataShop. In J. Kay, S. Bull, G. Biswas, & T. Mitrovic (Eds.), Proceeding of the 15th International Conference on Artificial Intelligence in Education (AIED2011) (pp. 557–559). Berlin: Springer.Google Scholar
  98. Thiede, K. W., Griffin, T. D., Wiley, J., & Redford, J. (2009). Metacognitive monitoring during and after reading. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 85–106). The Educational Psychology Series. New York: Routledge.Google Scholar
  99. Tousignant, M., & DesMarchais, J. E. (2002). Accuracy of student self-assessment ability compared to their own performance in a problem-based learning medical program: a correlation study. Advances in Health Sciences Education, 7, 19–27.CrossRefGoogle Scholar
  100. VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227–265.Google Scholar
  101. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.CrossRefGoogle Scholar
  102. VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992). A model of the self-explanation effect. Journal of the Learning Sciences, 2(1), 1–60.CrossRefGoogle Scholar
  103. VanLehn, K., Lynch, C., Schultz, K., Shapiro, J. A., Shelby, R. H., Taylor, L., et al. (2005). The Andes physics tutoring system: Lessons learned. International Journal of Artificial Intelligence in Education, 15(3), 147–204.Google Scholar
  104. White, B., & Frederiksen, J. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–117.CrossRefGoogle Scholar
  105. Winne, P. H. (1995). Inherent details in self-regulated learning. Educational Psychologist, 30, 173–187.CrossRefGoogle Scholar
  106. Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267–276. doi: 10.1080/00461520.2010.517150.CrossRefGoogle Scholar
  107. Winne, P. H. (2011). A cognitive and metacognitive analysis of self-regulated learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 15–32). New York: Routledge.Google Scholar
  108. Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 279–306). Hillsdale, NJ: Erlbaum.Google Scholar
  109. Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297–314). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  110. Winne, P. H., & Jamieson-Noel, D. (2002). Exploring students’ calibration of self reports about study tactics and achievement. Contemporary Educational Psychology, 27, 551–572.CrossRefGoogle Scholar
  111. Winne, P. H., Zhou, M., & Egan, R. (2011). Designing assessments of self-regulated learning. In G. Schraw & D. H. Robinson (Eds.), Assessment of higher-order thinking skills (pp. 89–118). Charlotte, NC: Information Age.Google Scholar
  112. Wittwer, J., & Renkl, A. (2008). Why instructional explanations often do not work: A framework for understanding the effectiveness of instructional explanations. Educational Psychologist, 43(1), 49–64.CrossRefGoogle Scholar
  113. Wood, H., & Wood, D. (1999). Help seeking, learning and contingent tutoring. Computers & Education, 33(2/3), 153–169.CrossRefGoogle Scholar
  114. Woolf, B. P. (2009). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Burlington, MA: Morgan Kaufmann.CrossRefGoogle Scholar
  115. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego, CA: Academic Press.CrossRefGoogle Scholar
  116. Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183.CrossRefGoogle Scholar
  117. Zimmerman, B. J. (2011). Motivational sources and outcomes of self-regulated learning and performance. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 49–64). New York: Routledge.Google Scholar
  118. Zimmerman, B. J., & Martinez-Pons, M. (1986). Development of a structured interview for assessing students’ use of self-regulated learning strategies. American Educational Research Journal, 23, 614–628.CrossRefGoogle Scholar
  119. Zusho, A., Karabenick, S. A., Bonney, C. R., & Sims, B. C. (2007). Contextual determinants of motivation and help seeking in the college classroom. In R. P. Perry & J. C. Smart (Eds.), The scholarship of teaching and learning in higher education: An evidence-based perspective (pp. 611–59). Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Human-Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations