Adaptive Technologies

Handbook of Research on Educational Communications and Technology, Third Edition

Volumn , Issue , 2008, Pages 277-294

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

^a FLORIDA STATE UNIVERSITY   (United States)

^b EDUCATIONAL TESTING SERVICE   (United States)

Author keywords

Adaptivity: The capability exhibited by an organic or an artificial organism to alter its behavior according to the environment. In the context of an instructional system, this capability allows the system to alter its behavior according to learner needs and other characteristics. This is typically represented within a learner model.; Hard technologies: These represent devices may be used in adaptive systems to capture learner information (e.g., eye tracking devices) or to present content to a learner (e.g., tactile tablet). These devices can be used to detect and classify learners performance data or affective states such as confusion, frustration, excitement, disappointment, boredom, confidence, contentment, and so on.; Learner model: A representation of the learner that is maintained by an adaptive system. Learner models can be used to provide personalized instruction to a particular individual, and may include cognitive and noncognitive aspects of the learner. Learner models have been used in many areas, such as adaptive educational and training systems, help systems, recommender systems, and others.; Soft technologies: These are usually algorithms, programs, or even environments that broaden the types of interaction between students and computers; for example, an adaptive algorithm may be employed in a program that selects an assessment task that provides the most information about a particular learner at a particular point in time.

Indexed keywords

EID: 85144949083     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9780203880869-27     Document Type: Chapter

References (112)

1. Alexander, P. A. and Judy, J. E. (1988). The interaction of domain-specific and strategic knowledge in academic performance. Rev. Educ. Res., 58(4), 375-404.
2. Anderson, J. R. (1993). The Adaptive Character of Thought. Hillsdale, NJ: Lawrence Erlbaum Associates.*
3. Anderson, J. R. and Lebiere, C. (1998). The Atomic Components of Thought. Mahwah, NJ: Lawrence Erlbaum Associates.
4. Anderson, J. R., Boyle, C. F., Corbett, A. T., and Lewis, M. (1990). Cognitive modeling and intelligent tutoring, Artif. Intell., 42, 7-49.
5. Anderson, J. R., Corbett, A. T., Koedinger, K. R., and Pelletier, R. (1995). Cognitive tutors: lessons learned. J. Learn. Sci., 4, 167-207.
6. Arroyo, I., Beal, C. R., Murray, T., Walles, R., and Woolf, B. P. (2004). Web-based intelligent multimedia tutoring for high stakes achievement tests. Proc. of ITS 2004: Intelligent Tutoring Systems, 7th Int. Conf., August 30-September 3, Maceiò, Alagoas, Brazil. Lect. Notes Comput. Sci., 3220, 468-477.
7. Arroyo, I., Woolf, B. P., and Beal, C. R. (2006). Addressing cognitive differences and gender during problem solving, Technol. Instruct. Cognit. Learn., 3(1), 31-63.
8. Azambuja Silveira, R. and Vicari, R. M. (2002). Developing distributed intelligent learning environment with JADE: Java Agents for Distance Education framework. Intell. Tutoring Syst., 2363, 105-118.
9. Biswas, G., Schwartz, D. L., Bransford, J., and the Teachable Agent Group at Vanderbilt (TAG-V). (2001). Technology support for complex problem solving: from SAD environments to AI. In Smart Machines in Education: The Coming Revolution in Educational Technology, edited by K. D. Forbus and P. J. Feltovich, pp. 71-97. Menlo Park, CA: AAAI/MIT Press.
10. Bredeweg, B. and Forbus, K. (2003). Qualitative modeling in education. AI Mag., 24(4), 35-46.
11. Bretzke H. and Vassileva, J. (2003) Motivating cooperation in peer-to-peer networks. In Proceedings from the User Modeling UM03 Conference, 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 Model. User-Adap. Interact., 6(2-3), 87-129.
14. Brusilovsky, P. (2001). Adaptive hypermedia. User Model. User-Adap. Interact., 11(1/2), 87-110.*
15. Brusilovsky, P., Sosnovsky, S., and Shcherbinina, O. (2005). User modeling in a distributed e-learning architecture. In Proceedings of the 10th International User Modeling Conference, edited by L. Ardissono, P. Brna, and A. Mitrovic, pp. 387-391. Berlin: Springer-Verlag.
16. Buxton, W. (2006). A Directory of Sources for Input Technologies. (http://www.billbuxton.com/InputSources.html).
17. Cassell, J., Nakano, Y., Bickmore, T., Sidner, C., and Rich, C. (2001). Annotating and generating posture from discourse structure in embodied conversational agents. In Proceedings of Workshop on Representing, Annotating, and Evaluating Non-Verbal and Verbal Communicative Acts to Achieve Contextual Embodied Agents, Autonomous Agents, Montreal, Quebec.
18. Chu, C. and Cohen, I. (2005). Posture and gesture recognition using 3D body shapes decomposition. In Proceedings of IEEE Workshop on Vision for Human-Computer Interaction (V4HCI), June 21, San Diego, CA (http://iris.usc.edu/~icohen/pdf/Wayne-v4hci05.pdf).
19. Conati, C. (2002). Probabilistic assessment of user’s emotions in educational games. J. Appl. Artif. Intell., 16(7-8), 555-575.
20. Conati, C. (2006). What to Adapt, and How? Personal communication, May 18, 2006.
21. Conati, C., Gertner, A., and VanLehn, K. (2002). Using Bayesian networks to manage uncertainty in student modeling. User Model. User-Adap. Interact., 12(4), 371-417.*
22. Conati, C., Merten, C., Muldner, K., and Ternes, D. (2005). Exploring eye tracking to increase bandwidth in user modeling. Lect. Notes Artif. Intell., 3538, 357-366.
23. Conchas, G. (2006). The Color of Success: Race and High Achieving Urban Youth. New York: Teachers College Press.
24. Cook, R. and Kay, J. (1994). The justified user model: a viewable, explained user model. In Proceedings of the Fourth International Conference on User Modeling (UM94), edited by A. Kobsa, and D. Litman, pp. 145-150, Hyannis, MA: MITRE, UM Inc.
25. Craig, S. D., Graesser, A. C., Sullins, J., and Gholson, B. (2004). Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. J. Educ. Media, 29(3), 241-250.
26. Cumming, G. and Self, J. (1989). Collaborative intelligent educational systems. In Proceedings of Artificial Intelligence and Education, edited by D. Bierman, J. Breuker, and J. Sandberg, pp. 73-80. Amsterdam: IOS.
27. Desimone, L. (1999). Linking parent involvement with student achievement: do race and income matter? J. Educ. Res., 93(1), 11-30.
28. Ekman, P. (2003). Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. New York: Henry Holt.
29. Fan, X. and Chen, M. (2001). Parental involvement and students’ academic achievement: a meta-analysis. Educ. Psychol. Rev., 13(1), 1-22.
30. Fink, J. and Kobsa, A. (2000). A review and analysis of commercial user modeling servers for personalization on the World Wide Web. User Model. User-Adap. Interact., 10, 209-249.
31. Frederiksen, J. and White, B. (2002). Conceptualizing and constructing linked models: creating coherence in complex knowledge systems. In The Role of Communication in Learning to Model, edited by P. Brna, M. Baker, K. Stenning, and A. Tiberghien, pp. 69-96. Mahwah, NJ: Lawrence Erlbaum Associates.*
32. 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), Master’s thesis. Graz, Austria: Graz University of Technology.
33. García-Barrios, V. M., Gütl, C., Preis, A., Andrews, K., Pivec, M., Mödritscher, F., and Trummer, C. (2004). AdeLE: a framework for adaptive e-learning through eye tracking. In Proceedings of the International Conference on Knowledge Management (I-KNOW '04), June 30-July 1, Graz, Austria, pp. 609-616.
34. Glaser, R. (1984). Education and thinking: the role of knowledge. Am. Psychol., 39(2), 93-104.*
35. González, G., Angulo, C., López, B., and de la Rosa, J. L. (2005). Smart user models: modelling the humans in ambient recommender systems. In Proceedings of the Workshop on Decentralized, Agent Based and Social Approaches to User Modelling (DASUM 2005), July 25, Edinburgh, pp. 11-20.
36. Graesser, A. C., Person, N., Harter, D., and Tutoring Research Group (TRG). (2001). Teaching tactics and dialog in Auto-Tutor. Int. J. Artif. Intell. Educ., 12, 257-279.
37. Greer, J. and Brooks, C. (2006). What to Adapt, and How? Personal communication, May 16, 2006.
38. Greer, J. E. and McCalla, G. I., Eds. (1994). Student Modelling: The Key to Individualized Knowledge-Based Instruction, Berlin: Springer Verlag.*
39. Hansen, E. G. and Mislevy, R. J. (2005). Accessibility of computer-based testing for individuals with disabilities and English language learners within a validity framework. In Online Assessment and Measurement: Foundation, Challenges, and Issues, edited by M. Hricko and S. Howell, pp. 212-259. Hershey, PA: Idea Group.
40. Hansen, E. G., Mislevy, R. J., Steinberg, L. S., Lee, M. J., and Forer, D. C. (2005). Accessibility of tests for individuals with disabilities within a validity framework. Syst. Int. J. Educ. Technol. Appl. Linguist., 33(1), 107-133.
41. Henze, N. and Nejdl, W. (2003). Logically characterizing adaptive educational hypermedia systems. In Proc. of Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems (AH2003), May 20-24, Budapest, Hungary.
42. Jameson, A. (1995). Numerical uncertainty management in user and student modeling: an overview of systems and issues. User Model. User-Adap. Interact., 5(3-4), 193-251
43. Jameson, A. (2006a). Adaptive interfaces and agents. In Human-Computer Interaction Handbook, 2nd ed., edited by J. A. Jacko and A. Sears, pp. 305-330. Mahwah, NJ: Lawrence Erlbaum Associates.
44. Jameson, A. (2006b). Challenges and Future of Learner Modeling. Personal communication, May 24, 2006.
45. Johnson, W. L. and Rickel, J. (1997). Steve: An animated pedagogical agent for procedural training in virtual environments. ACM SIGART Bull., 8(1-4), 16-21.
46. Johnson, W. L., Rickel, J. W., and Lester, J. C. (2000). Animated pedagogical agents: face-to-face interaction in interactive learning environments. Int. J. Artif. Intell. Educ., 11(1), 47-78.
47. Johnson, W. L., Beal, C., Fowles-Winkler, A., Narayanan, S., Papachristou, D., Marsella, S., and Vilhjálmsson, H. (2004). Tactical language training system: an interim report. Lect. Notes Comput. Sci., 3220, 336-345.
48. Kabassi, K. and Virvou, M. (2003). Using Web services for personalised Web-based learning. Educ. Technol. Soc., 6(3), 61-71.
49. Kapoor, A. and Picard, R. W. (2002). Real-Time, Fully Automatic Upper Facial Feature Tracking. Paper presented at the 5th IEEE International Conference on Automatic Face and Gesture Recognition, May 20-21, Washington, D.C.
50. Kay, J. (1999). A Scrutable User Modelling Shell for User-Adapted Interaction. Ph.D. thesis. Sydney, Australia: Basser Department of Computer Science, University of Sydney.
51. Kay, J. (2000). Stereotypes, student models and scrutability. Lect. Notes Comput. Sci., 1839, 19-30.
52. Kay, J. (2006). Challenges and Future of Learner Modeling. Personal communication, June 6, 2006.
53. Kettebekov, S., Yeasin, M., and Sharma, R. (2003). Improving continuous gesture recognition with spoken prosody. In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 16-22, Madison, WI, pp. 565-570.
54. Kinshuk and Lin, T. (2004). Cognitive profiling towards formal adaptive technologies in Web-based learning communities. Int. J. WWW-Based Communities, 1(1), 103-108.
55. Kobsa, A. (1993). User modeling: recent work, prospects and hazards. In Adaptive User Interfaces: Principles and Practice, edited by T. K. M. Schneider-Hufschmidt and U. Malinowski, pp. 111-128. Amsterdam: North-Holland.
56. Kobsa, A. (2002). Personalization and international privacy. Commun. ACM, 45(5), 64-67.
57. Kobsa, A. (2006). Generic user modeling systems and servers. In The Adaptive Web: Methods and Strategies of Web Personalization, edited by P. Brusilovsky, A. Kobsa, and W. Neijdl. Berlin: Springer-Verlag.
58. Koedinger, K. R. and Anderson, J. R. (1998). Illustrating principled design: the early evolution of a cognitive tutor for algebra symbolization. Interact. Learn. Environ., 5, 161-180.
59. Koedinger, K. R., Anderson, J. R., Hadley, W. H., and Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. Int. J. Artif. Intell. Educ., 8, 30-43.
60. Leelawong, K., Wang, Y., Biswas, G., Vye, N., and Bransford, J. (2001). Qualitative reasoning techniques to support learning by teaching: the teachable agents project. In Proceedings of the Fifteenth International Workshop on Qualitative Reasoning, May 17-18, St. Mary’s University, San Antonio, TX.
61. Litman, D. J. and Forbes-Riley, K. (2004). Predicting student emotions in computer-human tutoring dialogues. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL), July 21-26, Barcelona, pp. 351-358.
62. Matsuda, N., Cohen, W. W., and Koedinger, K. R. (2005). An intelligent authoring system with programming by demonstration. In Proc. of the Japan National Conference on Information and Systems in Education. Kanazawa, Japan.
63. McCalla. G. I. (2004). The ecological approach to the design of e-learning environments: purpose-based capture and use of information about learners. J. Interact. Media Educ., 7, 1-23 (special issue on the educational Semantic Web; http://www-jime.open.ac.uk/2004/7/mccalla-2004-7.pdf).
64. McCalla, G. I. (2006). Challenges and Future of Learner Modeling. Personal communication, May 26, 2006.
65. McCalla, G. I., Vassileva, J., Greer, J. E., and Bull, S. (2000). Active learner modeling. In Proceedings of ITS’2000, edited by G. Gauthier, C. Frasson, and K. VanLehn, pp. 53-62. Berlin: Springer-Verlag.
66. Merten, C. and Conati, C. (2006). Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments. In Proc. of Int. Conf. on Intelligent User Interfaces (IUI’06), January 29-February 1, Sydney, Australia (http://www.cs.ubc.ca/~conati/my-papers/IUI06eyetrackingCamera.pdf).
67. Mislevy, R. J., Steinberg, L. S., and Almond, R. G. (2003). On the structure of educational assessments. Meas. Interdisciplinary Res. Perspect., 1(1), 3-62.
68. Mitrovic, A. (2006). What to Adapt, and How? Personal communication, May 17, 2006.
69. Park, O. and Lee, J. (2003). Adaptive instructional systems. In Handbook of Research for Educational Communications and Technology, edited by D. H. Jonassen, pp. 651-685. Mahwah, NJ: Lawrence Erlbaum Associates.*
70. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Kaufmann.
71. Picard, R. W. (1997). Affective Computing. Cambridge, MA: MIT Press.
72. Potamianos, A., Narayanan, S., and Riccardi, G. (2005). Adaptive categorical understanding for spoken dialogue systems. IEEE Trans. Speech Audio Process., 13, 321-329.
73. Project LISTEN. (2006). http://www.cs.cmu.edu/~listen/.
74. Qu, L., Wang, N., and Johnson, W. L. (2005). Detecting the learner’s motivational states in an interactive learning environment. In Artificial Intelligence in Education, edited by C.-K. Looi et al., pp. 547-554, IOS Press.
75. Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychol. Bull., 124, 372-422.
76. Rich, E. (1979). User modeling via stereotypes. Cognit. Sci., 3(4), 329-354.
77. Rickel, J. and Johnson, W. L. (1997). Intelligent tutoring in virtual reality. In Proc. of Eighth World Conference on Artificial Intelligence in Education, August 19-22, Kobe, Japan, pp. 294-301.
78. Seo, K., Cohen, I., You, S., and Neumann, U. (2004). Face pose estimation system by combining hybrid ICA-SVM learning and re-registration. In Proc. of Asian Conference on Computer Vision (ACCV), January 27-30, Jeju Island, Korea.
79. Shaw, E., Johnson, W. L., and Ganeshan, R. (1999). Pedagogical agents on the Web. In Proc. of the Third Int. Conf. on Autonomous Agents, May 1-5, Seattle, WA, pp. 283-290.
80. Shute, V. J. (2006). Tensions, Trends, Tools, and Technologies: Time for an Educational Sea Change. Princeton, NJ: ETS.
81. Shute, V. J. and Glaser, R. (1990). Large-scale evaluation of an intelligent tutoring system: Smithtown. Interact. Learn. Environ., 1, 51-76.
82. Shute, V. J. and Psotka, J. (1996). Intelligent tutoring systems: past, present, and future. In Handbook of Research for Educational Communications and Technology, edited by D. Jonassen, pp. 570-600. New York: Macmillan.*
83. Shute, V. J. and Regian, J. W. (1993). Principles for evaluating intelligent tutoring systems. J. Artif. Intell. Educ., 4(3), 245-271.
84. Shute, V. J., Glaser, R., and Raghavan, K. (1989). Inference and discovery in an exploratory laboratory. In Learning and Individual Differences, edited by P. L. Ackerman, R. J. Sternberg, and R. Glaser, pp. 279-326. New York: W.H. Freeman.
85. Shute, V. J., Lajoie, S. P., and Gluck, K. A. (2000). Individualized and group approaches to training. In Training and Retraining: A Handbook for Business, Industry, Government, and the Military, edited by S. Tobias and J. D. Fletcher, pp. 171-207. New York: Macmillan.
86. Shute, V. J., Graf, E. A., and Hansen, E. (2005). Designing adaptive, diagnostic math assessments for individuals with and without visual disabilities. In Technology-Based Education: Bringing Researchers and Practitioners Together, edited by L. PytlikZillig, R. Bruning, and M. Bodvarsson, pp. 169-202. Greenwich, CT: Information Age Publishing.
87. Sison, R. and Shimura, M. (1998). Student modeling and machine learning. Int. J. Artif. Intell. Educ., 9, 128-158.
88. SNOW. (2006). http://snow.utoronto.ca/technology/.
89. Snow, C. E. and Biancarosa, G. (2003). Adolescent Literacy and the Achievement Gap: What Do We Know and Where Do We Go from Here? New York: Carnegie.
90. Snow, R. E. (1989). Toward assessment of cognitive and conative structures in learning. Educ. Res., 18(9), 8-14.
91. Snow, R. E. (1994). Abilities in academic tasks. In Mind in Context: Interactionist Perspectives on Human Intelligence, edited by R. J. Sternberg and R. K. Wagner, pp. 3-37. Cambridge, U.K.: Cambridge University Press.
92. Soller, A. (2004). Computational modeling and analysis of knowledge sharing in collaborative distance learning, User Model. User-Adap. Interact., 14(4), 351-381.
93. Stansfield, J., Carr, B., and Goldstein, I. (1976). Wumpus Advisor: A First Implementation of a Program That Tutors Logical and Probabilistic Reasoning Skills, Technical Report 381. Cambridge, MA: Artificial Intelligence Laboratory, MIT.
94. Teasley, S. D. and Roschelle, J. (1993). Constructing a joint problem space: the computer as a tool for sharing knowledge. In Computers as Cognitive Tools, edited by S. P. Lajoie and S. J. Derry, pp. 229-258. Hillsdale, NJ: Lawrence Erlbaum Associates.
95. Tobias, S. (1994). Interest, prior knowledge, and learning. Rev. Educ. Res., 64(1), 37-54.
96. Trella, M., Carmona, C., and Conejo, R. (2005). MEDEA: an open service-based learning platform for developing intelligent educational systems for the Web. In Proc. of Workshop on Adaptive Systems for Web-Based Education at 12th Int. Conf. on Artificial Intelligence in Education (AIED 2005), July 18-22, Amsterdam, pp. 27-34.
97. VanLehn, K., Lynch, C., Schulze, K., Shapiro, J. A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., and Wintersgill, M. (2005). The Andes physics tutoring system: lessons learned. Int. J. Artif. Intell. Educ., 15(3), 147-204.
98. Vassileva, J. (1998). DCG+GTE: dynamic courseware generation with teaching expertise. Instruct. Sci., 26(3/4), 317-332.
99. Vassileva, J. (2006). What to Adapt, and How? Personal communication, May 15, 2006.
100. Vassileva, J. and Wasson, B. (1996). Instructional planning approaches: from tutoring towards free learning. In Proc. of EuroAIED, Sept. 30-Oct. 2, Lisbon, Portugal, pp. 1-8.
101. Vassileva, J., McCalla, G. I., and Greer, J. E. (2003). Multiagent multi-user modeling in I-Help. User Model. User-Adap. Interact., 13 (1-2), 179-210.
102. Webb, G., Pazzani, M. J., and Billsus, D. (2001). Machine learning for user modeling. User Model. User-Adap. Interact., 11, 19-29.
103. Wenger, E. (1987). Artificial Intelligence and Tutoring Systems. Los Altos, CA: Morgan Kaufmann.
104. Wickens, C. D. and Hollands, J. G. (2000). Engineering Psychology and Human Performance, 3rd ed. Upper Saddle River, NJ: Prentice Hall.
105. Winter, M., Brooks, C., and Greer, J. (2005) Towards best practices for Semantic Web student modelling. In Proc. of the 12th Int. Conf. on Artificial Intelligence in Education (AIED 2005), July 18-22, Amsterdam.
106. Woolf, B. (2006). What to Adapt, and How? Personal communication, May 22, 2006.
107. Wright, I. (1997). Emotional Agents. Ph.D. thesis. Birmingham, U.K.: University of Birmingham (http://citeseer.ist.psu.edu/wright97emotional.html).
108. Yang, M., Zapata-Rivera, D., and Bauer, M. (2006). E-grammar: an assessment-based learning environment for English grammar. In Proc. of the Annual Conference of ED-Media, June 26-30, Orlando, FL.
109. Yeasin, M. and Bullot, B. (2005). Comparison of linear and non-linear data projection techniques in recognizing universal facial expressions. Proc. Int. Joint Conf. Neural Netw., 5, 3087-3092.
110. Zapata-Rivera, D. and Greer, J. (2000). Inspecting and visualizing distributed Bayesian student models. In Proc. of the 5th Int. Conf. on Intelligent Tutoring Systems (ITS 2000), June 19-23, Montreal, Canada, pp. 544-553.
111. Zapata-Rivera, D. and Greer, J. (2004) Inspectable Bayesian student modelling servers in multi-agent tutoring systems, Int. J. Hum.-Comput. Stud., 61(4), 535-563.
112. Zelinsky, A. and Heinzmann, J. (1996). Real-time visual recognition of facial gestures for human-computer interaction. In Proc. of the Second Int. Conf. on Automatic Face and Gesture Recognition, October 13-16, Killington, VT.*