ADAPTIVE TECHNOLOGIES

ETS Research Report Series

Volumn 2007, Issue 1, 2007, Pages i-34

  Shute, Valerie J ^a   Zapata Rivera, Diego ^a  

^a EDUCATIONAL TESTING SERVICE   (United States)

Author keywords

adaptivity; hard technologies; learner model; soft technologies

Indexed keywords

EID: 85164452317     PISSN: None     EISSN: 23308516     Source Type: Journal    
DOI: 10.1002/j.2333-8504.2007.tb02047.x     Document Type: Article

References (104)

1. Alexander, P. A., & Judy, J. E. (1988). The interaction of domain-specific and strategic knowledge in academic performance. Review of Educational Research, 58(4), 375–404.
2. Anderson, J. R. (1993). The adaptive character of thought. Hillsdale, NJ: Lawrence Erlbaum.
3. Anderson, J. R., Boyle, C. F., Corbett, A. T., & Lewis, M. (1990). Cognitive modeling and intelligent tutoring. Artificial Intelligence, 42, 7–49.
4. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4, 167–207.
5. Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence Erlbaum.
6. Arroyo, I., Beal, C. R., Murray, T., Walles, R., & Woolf, B. P. (2004). Web-based intelligent multimedia tutoring for high stakes achievement tests. In C. Lester, R. M. Vicari, & F. Paraguacu (Eds.), Lecture notes in computer science: Vol. 3220. Intelligent tutoring systems (pp. 468–477). Berlin: Springer-Verlag.
7. Arroyo, I., Woolf, B. P., & Beal, C. R. (2006). Addressing cognitive differences and gender during problem solving. Technology, Instruction, Cognition & Learning, 3(1), 31–63.
8. Azambuja Silveira, R., & Vicari, R. M. (2002). Developing distributed intelligent learning environment with JADE—Java Agents for Distance Education Framework. Intelligent Tutoring Systems, 2363, 105–118.
9. Biswas, G., Schwartz, D., Bransford, J., and the Teachable Agent Group at Vanderbilt. (2001). Technology support for complex problem solving: From SAD environments to AI. In K. D. Forbus and P. J. Feltovich (Eds.), Smart machines in education: The coming revolution in educational technology (pp. 71–97). Menlo Park, CA: AAAI/MIT Press.
10. Bredeweg, B. & Forbus, K. (2003). Qualitative modeling in education. AI Magazine, 24(4), 35–46.
11. Bretzke, H., & Vassileva J. (2003). Motivating cooperation in peer-to-peer networks. In Brusilovsky, A. Corbett, & F. de Rosis (Eds.), Lecture Notes in Artificial Intelligence: Vol 2701: User Modeling 2003 (pp. 218–227). Berlin: Springer-Verlag.
12. Brooks, R. A. (1999). Cambrian intelligence: The early history of the new AI. Cambridge, MA: MIT Press.
13. Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User Adapted Interaction, 6(2/3), 87–129.
14. Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1/2), 87–110.
15. Brusilovsky, P., Sosnovsky, S., & Shcherbinina, O. (2005). User modeling in a distributed e-learning architecture. In L. Ardissono, P. Brna, & A. Mitrovic (Eds.), Lecture Notes in Artificial Intelligence: Vol 3538. User modeling 2005 (pp. 387–391). Berlin: Springer-Verlag.
16. Buxton, W. (2006). A directory of sources for input technologies. Retrieved October 3, 2006, from http://www.billbuxton.com/InputSources.html.
17. Cassell, J., Nakano, Y., Bickmore, T., Sidner, C., & Rich, C. (2001, May). Annotating and generating posture from discourse structure in embodied conversational agents. Paper presented at the workshop on representing, annotating, and evaluating non-verbal and verbal communicative acts to achieve contextual embodied agents, Montreal, Quebec, Canada.
18. Chu, C., & Cohen, I. (2005, June 21). Posture and gesture recognition using 3D body shapes decomposition. Paper presented at the IEEE workshop on vision for human-computer tnteraction (V4HCI). Retrieved October 2, 2006, from http://iris.usc.edu/~icohen/pdf/Wayne-v4hci05.pdf
19. Conati, C. (2002). Probabilistic assessment of user's emotions in educational games. Journal of Applied Artificial Intelligence, 16(7/8), 555–575.
20. Conati, C., Gertner, A., & VanLehn, K. (2002). Using Bayesian networks to manage uncertainty in student modeling. Journal of User Modeling and User-Adapted Interaction, 12(4), 371–417.
21. Conati, C., Merten, C., Muldner, K., & Ternes, D. (2005, July 26–30). Exploring eye-tracking to increase bandwidth in user modeling. Paper presented at UM2005 user modeling: The tenth international conference, Edinburgh, Scotland. Retrieved October 29, 2006, from http://www.cs.ubc.ca/~conati/my-papers/um05-eyetracking-camera.pdf
22. Conchas, G. (2006). The color of success: Race and high achieving urban youth. New York: Teachers College Press.
23. Cook, R., & Kay, J. (1994). The justified user model: A viewable, explained user model. In A. Kobsa & D. Litman (Eds.), Proceedings of the fourth international conference on user modeling (pp. 145–150). Hyannis, MA: MITRE, UM.
24. Craig, S. D., Graesser, A. C., Sullins, J., & Gholson, B. (2004). Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media, 29(3), 241–250.
25. Cumming, G., & Self, J. (1989). Collaborative intelligent educational systems. In D. Bierman, J. Breuker, & J. Sandberg (Eds.), Proceedings of Artificial Intelligence and Education (pp. 73–80). Amsterdam: IOS.
26. Desimone, L. (1999). Linking parent involvement with student achievement: Do race and income matter? Journal of Educational Research, 93(1), 11–30.
27. Ekman, P. (2003). Emotions revealed: Recognizing faces and feelings to improve communication and emotional life. New York: Henry Holt.
28. Fan, X., & Chen, M. (2001). Parental involvement and students' academic achievement: A meta-analysis. Educational Psychology Review, 13(1), 1–22.
29. Fink, J., & Kobsa, A. (2000). A review and analysis of commercial user modeling servers for personalization on the World Wide Web. User Modeling and User-Adapted Interaction, 10, 209–249.
30. Frederiksen, J., & White, B. (2002). Conceptualizing and constructing linked models: Creating coherence in complex knowledge systems. In P. Brna, M. Baker, K. Stenning, & A. Tiberghien (Eds.), The role of communication in learning to model (pp. 69–96). Mahwah, NJ: Lawrence Erlbaum.
31. Fröschl, C. (2005). User modeling and user profiling in adaptive e-learning systems: An approach for a service-based personalization solution for the research project AdeLE (Adaptive e-Learning with Eye-Tracking). Unpublished master's thesis, Graz University of Technology, Graz, Austria.
32. García-Barrios, V. M., Gütl, C., Preis, A., Andrews, K., Pivec, M., Mödritscher, F., et al. (2004). AdELE: A framework for adaptive e-learning through eye tracking. In Proceedings of I-KNOW '04 (pp. 609–616). Graz, Austria.
33. Glaser, R. (1984). Education and thinking: The role of knowledge. American Psychologist, 39(2), 93–104.
34. González, G., Angulo, C., López, B., & de la Rosa, J. L. (2005). Smart user models: Modelling the humans in ambient recommender systems. In P. Dolog & J. Vassileva (Eds.), Proceedings of the workshop on decentralized, agent based and social approaches to user modelling (DASUM 2005; pp. 11–20), Edinburgh, Scotland.
35. Graesser, A. C., Person, N., Harter, D., & the Tutoring Research Group. (2001). Teaching tactics and dialog in AutoTutor. International Journal of Artificial Intelligence in Education, 12, 257–279.
36. Greer, J. E., & McCalla, G. I. (Eds.). (1994). Student modelling: The key to individualized knowledge-based instruction, Berlin: Springer-Verlag.
37. Hansen, E. G., & Mislevy, R. J. (2005). Accessibility of computer-based testing for individuals with disabilities and English language learners within a validity framework. In M. Hricko & S. Howell (Eds.), Online assessment and measurement: Foundations and challenges (pp. 214–262). Hershey, PA: Idea Group.
38. Hansen, E. G., Mislevy, R. J., Steinberg, L. S., Lee, M. J., & Forer, D. C. (2005). Accessibility of tests for individuals with disabilities within a validity framework. System: An International Journal of Educational Technology and Applied Linguistics, 33(1), 107–133.
39. Henze, N., & Nejdl, W. (2003). Logically characterizing adaptive educational hypermedia systems. In Proceedings of the AH 2003: Workshop on adaptive hypermedia and adaptive Web-based systems (pp. 15–28). Eindoven, The Netherlands: Eindoven University of Technology.
40. Jameson, A. (1995). Numerical uncertainty management in user and student modeling: An overview of systems and issues. User Modeling and User-Adapted Interaction, 5(3/4), 193–251.
41. Jameson, A. (2006). Adaptive interfaces and agents. In J. A. Jacko & A. Sears (Eds.), Human-computer interaction handbook (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
42. Johnson, W. L., Beal, C., Fowles-Winkler, A., Narayanan, S., Papachristou, D., Marsella, S., et al. (2004). Tactical language training system: An interim report. In J. C. Lester, R. M. Vicari, & F. Parguacu (Eds.), Lecture notes in computer science: Vol. 3220: Intelligent tutoring systems (pp. 336–345). Berlin: Springer-Verlag.
43. Johnson, W. L., & Rickel, J. (1997). Steve: An animated pedagogical agent for procedural training in virtual environments. ACM SIGART Bulletin, 8(1/4), 16–21.
44. Johnson, W. L., Rickel, J. W., & Lester, J. C. (2000). Animated pedagogical agents: Face-to-face interaction in interactive learning environments. International Journal of Artificial Intelligence in Education, 11(1), 47–78.
45. Kabassi, K., & Virvou, M. (2003). Using Web services for personalised Web-based learning. Educational Technology & Society, 6(3), 61–71. Retrieved October 10, 2006, from http://ifets.ieee.org/periodical/6_3/8.html
46. Kapoor, A., & Picard, R. W. (2002, May). Real-time, fully automatic upper facial feature tracking. In Proceedings of the 5th IEEE international conference on automatic face and gesture recognition (pp. 10–15). Los Alamitos, CA: IEEE Press.
47. Kay, J. (1999). A scrutable user modelling shell for user-adapted interaction. Unpublished doctoral dissertation, Basser Department of Computer Science, University of Sydney, Sydney, Australia.
48. Kay, J. (2000). Stereotypes, student models and scrutability. In G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Lecture notes in computer science: Vol. 1839. Intelligent tutoring systems (pp. 19–30). Berlin: Springer-Verlag.
49. Kettebekov, S., Yeasin, M., & Sharma, R. (2003). Improving continuous gesture recognition with spoken prosody. In 2003 IEEE Computer Society conference on computer vision and pattern recognition (CVPR '03; vol. 1; pp. 565–570). Los Alamitos, CA: IEEE.
50. Kinshuk, & Lin, T. (2004). Cognitive profiling towards formal adaptive technologies in Web-based learning communities. International Journal of WWW-Based Communities, 1(1), 103–108.
51. Kobsa, A. (1993). User modeling: Recent work, prospects and hazards. In T. K. M. Schneider-Hufschmidt & U. Malinowski (Eds.), Adaptive user interfaces: Principles and practice (pp. 111–128). Amsterdam: Elsevier.
52. Kobsa, A. (2002). Personalization and international privacy. Communications of the ACM, 45(5), 64–67.
53. Kobsa, A. (in press). Generic user modeling systems and servers. In P. Brusilovsky, A. Kobsa, & W. Neijdl (Eds.), Lecture notes in computer science: Vol. 4321. The adaptive Web: Methods and strategies of Web personalization. Berlin: Springer-Verlag.
54. Koedinger, K. R., & Anderson, J. R. (1998). Illustrating principled design: The early evolution of a cognitive tutor for algebra symbolization. Interactive Learning Environments, 5, 161–180.
55. 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.
56. Leelawong, K., Wang, Y., Biswas, G., Vye, N., & Bransford, J. (2001). Qualitative reasoning techniques to support learning by teaching: The teachable agents project. In G. Biswas (Ed.), Proceedings of the fifteenth international workshop on qualitative reasoning (pp. 32–43). Menlo Park, CA: AAAI Press.
57. Litman, D. J., & Forbes-Riley, K. (2004). Predicting student emotions in computer-human tutoring dialogues. Proceedings of the 42nd annual meeting of the Association for Computational Linguistics (pp. 351–358). San Francisco, CA: Morgan-Kaufman.
58. Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2005). An intelligent authoring system with programming by demonstration. In Proceedings of the Japan national conference on information and systems in education. Kanazawa, Japan. Retrieved October 31, 2006, from http://www.cs.cmu.edu/~wcohen/postscript/japan-ws-2005.pdf
59. McCalla, G. I., Vassileva, J., Greer, J. E., & Bull, S. (2000). Active learner modeling. In G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Lecture notes in computer science: Vol. 1839. Intelligent tutoring systems (pp. 53–62.), Berlin: Springer-Verlag.
60. Merten, C., & Conati, C. (2006). Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments. Paper presented at the international conference on intelligent user interfaces (IUI 06), Syndey, Australia. Retrieved October 31, 2006, from http://www.cs.ubc.ca/~conati/my-papers/IUI06eyetrackingCamera.pdf
61. Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). On the structure of educational assessments. Measurement: Interdisciplinary Research and Perspectives, 1(1), 3–62.
62. Park, O., & Lee, J. (2003). Adaptive instructional systems. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 651–685). Mahwah, NJ: Lawrence Erlbaum.
63. Park, O., & Lee, J. (in press). Adaptive Instructional Systems. Technologies. In J. M. Spector, D. Merrill, J. van Merriënboer, & M. Driscoll (Eds.), Handbook of Research on Educational Communications and Technology (3rd ed.). Mahwah, NJ: Erlbaum Associates.
64. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Kaufmann.
65. Picard, R. W. (1997). Affective computing. Cambridge, MA: MIT Press.
66. Potamianos, A., Narayanan, S., & Riccardi, G. (2005). Adaptive categorical understanding for spoken dialogue systems. IEEE Transactions on Speech and Audio Processing, 13, 321–329.
67. Project LISTEN. (2005). Project Listen: A reading tutor that listens. Summary. Retrieved October 28, 2006, from http://www.cs.cmu.edu/~listen/
68. Qu, L., Wang N., & Johnson, W. L. (2005). Detecting the learner's motivational states in an interactive learning environment. In C.-K. Looi, G. McCalla, B. Bredeweg, & J. Breuker (Eds.), Frontiers in artificial intelligence and applications: Vol. 125: Artificial intelligence in education (pp. 547–554). Amsterdam: IOS Press.
69. Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372–422.
70. Rich, E. (1979). User modeling via stereotypes. Cognitive Science, 3(4), 329–354.
71. Rickel, J., & Johnson, W. L. (1997). Intelligent tutoring in virtual reality: A preliminary report. In Proceedings of the eighth international conference on artificial intelligence in education (pp. 294–301). Amsterdam: IOS Press.
72. Seo, K., Cohen, I., You, S., & Neumann, U. (2004, January). Face pose estimation system by combining hybrid ICA-SVM learning and re-registration. Paper presented at the Asian conference on computer vision (ACCV), Jeju, Korea.
73. Shaw, E., Johnson, W. L., & Ganeshan, R. (1999). Pedagogical agents on the Web. In Proceedings of the third international conference on autonomous agents (pp. 283–290). New York: ACM Press.
74. Shute, V. J. (2006). Tensions, trends, tools, and technologies: Time for an educational sea change (ETS RR-06-16). Princeton, NJ: ETS.
75. Shute, V. J., & Glaser, R. (1990). Large-scale evaluation of an intelligent tutoring system: Smithtown. Interactive Learning Environments, 1, 51–76.
76. Shute, V. J., Glaser, R., & Raghavan, K. (1989). Inference and discovery in an exploratory laboratory. In P. L. Ackerman, R. J. Sternberg, & R. Glaser (Eds.), Learning and individual differences (pp. 279–326). New York: W.H. Freeman.
77. Shute, V. J., Graf, E. A., & Hansen, E. (2005). Designing adaptive, diagnostic math assessments for individuals with and without visual disabilities. In L. PytlikZillig, R. Bruning, & M. Bodvarsson (Eds.), Technology-based education: Bringing researchers and practitioners together (pp. 169–202). Greenwich, CT: Information Age.
78. Shute, V. J., Lajoie, S. P., & Gluck, K. A. (2000). Individualized and group approaches to training. In S. Tobias & J. D. Fletcher (Eds.), Training and retraining: A handbook for business, industry, government, and the military (pp. 171–207). New York: Macmillan.
79. Shute, V. J., & Psotka, J. (1996). Intelligent tutoring systems: Past, present, and future. In D. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 570–600). New York: Macmillan.
80. Shute, V. J., & Regian, J. W. (1993). Principles for evaluating intelligent tutoring systems. Journal of Artificial Intelligence in Education, 4(3), 245–271.
81. Sison, R., & Shimura, M. (1998). Student modeling and machine learning. International Journal of Artificial Intelligence in Education, 9, 128–158.
82. Snow, C. E., & Biancarosa, G. (2003). Adolescent literacy and the achievement gap: What do we know and where do we go from here? New York: Carnegie Corporation of New York.
83. Snow, R. E. (1989). Toward assessment of cognitive and conative structures in learning. Educational Researcher, 18(9), 8–14.
84. Snow, R. E. (1994). Abilities in academic tasks. In R. J. Sternberg & R. K. Wagner (Eds.), Mind in context: Interactionist perspectives on human intelligence (pp. 3–37). New York: Cambridge University Press.
85. Special Needs Opportunity Windows. (2006). Adaptive technology. Retrieved October 28, 2006, from http://snow.utoronto.ca/technology/
86. Soller, A. (2004). Computational modeling and analysis of knowledge sharing in collaborative distance learning. User Modeling and User-Adapted Interaction, 14(4), 351–381.
87. Stansfield, J., Carr, B., & Goldstein, I. (1976). Wumpus advisor: A first implementation of a program that tutors logical and probabilistic reasoning skills (Technical Rep. No. 381). Cambridge, MA: MIT, Artificial Intelligence Laboratory.
88. Teasley, S. D., & Rochelle, J. (1993). Constructing a joint problem space: The computer as a tool for sharing knowledge. In S. P. Lajoie & S. J. Derry (Eds.), Computers as cognitive tools (pp. 229–258). Hillsdale, NJ: Lawrence Erlbaum.
89. Tobias, S. (1994). Interest, prior knowledge, and learning. Review of Educational Research, 64(1), 37–54.
90. Trella, M., Carmona, C., & Conejo, R. (2005). MEDEA: An open service-based learning platform for developing intelligent educational systems for the Web. In Proceedings of workshop on adaptive systems for Web based education at 12th international conference on artificial intelligence in education (pp. 27–34). Amsterdam: IOS Press.
91. VanLehn, K., Lynch, C., Schulze, K., Shapiro, J. A., Shelby, R., Taylor, L., et al. (2005). The Andes physics tutoring system: Lessons learned. International Journal of Artificial Intelligence and Education, 15(3), 147–204.
92. Vassileva, J. (1998). DCG +GTE: Dynamic courseware generation with teaching expertise. Instructional Science, 26(3/4), 317–332.
93. Vassileva, J., McCalla, G. I., & Greer, J. E. (2003). Multi-agent multi-user modeling in I-Help. User Modeling and User-Adapted Interaction, 13(1/2), 179–210.
94. Vassileva, J., & Wasson, B. (1996). Instructional planning approaches: From tutoring towards free learning. In P. Brna, A. Paiva & J. Self (Eds.), Proceedings of the European conference on artificial intelligence in education (EuroAIED; pp. 1–8). Lisbon, Portugal: Edições Colibri.
95. Webb, G., Pazzani, M. J., & Billsus, D. (2001). Machine learning for user modeling. User Modeling and User-Adapted Interaction, 11, 19–29.
96. Wenger, E. (1987). Artificial intelligence and tutoring systems. Los Altos, CA: Morgan Kaufmann.
97. Wickens, C. D., & Hollands, J. G. (2000). Engineering psychology and human performance (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
98. Winter, M., Brooks, C., & Greer, J. (2005, July). Towards best practices for semantic Web student modelling. Paper presented at the 12th international conference on artificial intelligence in education (AIED 2005), Amsterdam, The Netherlands.
99. Wright, I. (1997). Emotional agents. Unpublished doctoral dissertation, University of Birmingham, Birmingham, England.
100. Yang, M., Zapata-Rivera, D., & Bauer, M. (2006). E-grammar: An assessment-based learning environment for English grammar. In Proceedings of the world conference on educational multimedia/hypermedia & educational telecommunications (ED-MEDIA '06). Chesapeake, VA: Association for the Advancement of Computing in Education.
101. Yeasin, M., & Bullot, B. (2005). Comparison of linear and non-linear data projection techniques in recognizing universal facial expressions. In Proceedings of the IEEE international joint conference on neural networks (vol. 5; pp. 3087–3092). Washington, DC: IEEE Computer Society.
102. Zapata-Rivera, D., & Greer, J. (2000). Inspecting and visualizing distributed Bayesian student models. In G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Lecture notes in computer science: Vol. 1839. Intelligent tutoring systems (pp. 544–553). Berlin: Springer-Verlag.
103. Zapata-Rivera, D., & Greer, J. (2004). Inspectable Bayesian student modelling servers in multi-agent tutoring systems. International Journal of Human-Computer Studies, 61(4), 535–563.
104. Zelinsky, A., & Heinzmann, J. (1996). Real-time visual recognition of facial gestures for human-computer interaction. In Proceedings of the 2nd international conference on automatic face and gesture recognition (pp. 351–356). Washington, DC: IEEE Computer Society.