Computer learning environments with agents that support deep comprehension and collaborative reasoning

Generalization of Knowledge: Multidisciplinary Perspectives

Volumn , Issue , 2011, Pages 201-224

  Graesser, Arthur C ^a   Lin, David ^b   D’Mello, Sidney ^c  

^a University of Memphis   (United States)

^b Department of Computer Science University of Memphis ^*  (United States)

^c UNIVERSITY OF MEMPHIS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84908662461     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9780203848036     Document Type: Chapter

References (76)

1. Aleven, V., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26, 147–179.
2. American Association for the Advancement of Science. (1993). Benchmarks for science literacy: Project 2061. New York: Oxford University Press.
3. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4, 167–207.
4. Anderson, J. R., Reder, L. M., & Simon, H. A. (1996). Situated learning and education. Educational Researcher, 25, 5–11.
5. Ashley, K. D. (1990). Modeling legal argument: Reasoning with cases and hypotheticals. Cambridge, MA: MIT Press.
6. Atkinson, R. K. (2002). Optimizing learning from examples using animated pedagogical agents. Journal of Educational Psychology, 94, 416–427.
7. Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students’ learning with hypermedia? Journal of Educational Psychology, 96, 523–535.
8. Bandura, A. (1986). Social foundations of thought and action: A social cognition theory. Englewood Cliffs, NJ: Prentice Hall.
9. Baylor, A. L., & Kim, Y. (2005). Simulating instructional roles through pedagogical agents. International Journal of Artificial Intelligence in Education, 15, 95–115.
10. Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn. Washington, DC: National Academy Press.
11. Chi, M. T. H., de Leeuw, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.
12. Chi, M. T. H., Siler, S. A., & Jeong, H. (2004). Can tutors monitor students’ understanding accurately? Cognition and Instruction, 22, 363–387.
13. Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25, 471–533.
14. Cohen, P. A., Kulik, J. A., & Kulik, C. C. (1982). Educational outcomes of tutoring: A metaanalysis of findings. American Educational Research Journal, 19, 237–248.
15. Cole, R., van Vuuren, S., Pellom, B., Hacioglu, K., Ma, J., Movellan, J., et al. (2003). Perceptive animated interfaces: First steps toward a new paradigm for human computer interaction. Proceedings of the IEEE, 91, 1391–1405.
16. Corbett, A. T. (2001). Cognitive computer tutors: Solving the two-sigma problem. In User modeling: Proceedings of the Eighth International Conference (UM 2001, pp. 137–147). Berlin: Springer.
17. Craig, S. D., Gholson, B., Ventura, M., Graesser, A. C., & The Tutoring Research Group. (2000). Overhearing dialogues and monologues in virtual tutoring sessions: Effects on questioning and vicarious learning. International Journal of Artificial Intelligence in Education, 11, 242–253.
18. Cuban, L. (1986). Teachers and machines: The classroom use of technology since 1920. New York: Teachers College.
19. Cuban, L. (2001). Oversold and underused: Computers in the classroom. Cambridge, MA: Harvard University Press.
20. D’Mello, S. K., Craig, S. D., Gholson, B., Franklin, S., Picard, R., & Graesser, A. C. (2005). Integrating affect sensors in an intelligent tutoring system. In Affective Interactions: The Computer in the Affective Loop Workshop at 2005 International Conference on Intelligence Users Interfaces (pp. 7–13) New York: AMC Press.
21. D’Mello, S. K., Craig, S. D., & Graesser, A. C. (2006). Predicting affective states through an emote-aloud procedure from AutoTutor’s mixed-initiative dialogue. International Journal of Artificial Intelligence in Education, 16, 3–28.
22. Dodds, P., & Fletcher, J. (2004). Opportunities for new “smart” learning environments enabled by next-generation Web capabilities. Journal of Educational Multimedia and Hypermedia, 13, 391–404.
23. Driscoll, D. M., Craig, S. D., Gholson, B., Ventura, M., Hu, X., & Graesser, A. C. (2003). Vicarious learning: Effects of overhearing dialog and monolog-like discourse in a virtual tutoring session. Journal of Educational Computing Research, 29, 431–450.
24. Forbus, K., Gentner, D., & Law, K. (1995). MAC/FAC: A model of similarity-based retrieval. Cognitive Science, 19, 141–205.
25. Fox, B. (1993). The human tutorial dialogue project. Hillsdale, NJ: Lawrence Erlbaum.
26. Gee, J. (2003). What video games have to teach us about learning and literacy. New York: Palgrave Macmillan.
27. Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12, 306–355.
28. Graesser, A. C., Chipman, P., Haynes, B. C., & Olney, A. (2005). AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions in Education, 48, 612–618.
29. Graesser, A. C., D’Mello, S. K., Craig, S. D., Witherspoon, A., Sullins, J., McDaniel, B., & Gholson, B., (2008). The Relationship between Affective States and Dialogue Patterns during interactions with AutoTutor. Journal of Interactive Learning Research, 19(2), 293–312.
30. Graesser, A. C., Gernsbacher, M. A., & Goldman, S. (Eds.). (2003). Handbook of discourse processes. Mahwah, NJ: Erlbaum.
31. Graesser, A. C., Hu, X., Person, P., Jackson, T., & Toth, J. (2004). Modules and information retrieval facilities of the Human Use Regulatory Affairs Advisor (HURAA). International Journal on eLearning, 3, 29–39.
32. Graesser, A. C., Jackson, G. T., & McDaniel, B. (2007). AutoTutor holds conversations with learners that are responsive to their cognitive and emotional states. Educational Technology, Educational Technology, 47, 19–22.
33. Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H., Ventura, M., Olney, A., et al. (2004). AutoTutor: A tutor with dialogue in natural language. Behavioral Research Methods, Instruments, and Computers, 36, 180–193.
34. Graesser, A. C., McNamara, D. S., & VanLehn, K. (2005). Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART. Educational Psychologist, 40, 225–234.
35. Graesser, A. C., Moreno, K., Marineau, J., Adcock, A., Olney, A., & Person, N. (2003). AutoTutor improves deep learning of computer literacy: Is it the dialog or the talking head? In U. Hoppe, F. Verdejo, & J. Kay (Eds.), Proceedings of artificial intelligence in education (pp. 47–54). Amsterdam: IOS Press.
36. Graesser, A. C., & Olde, B. A. (2003). How does one know whether a person understands a device? The quality of the questions the person asks when the device breaks down. Journal of Educational Psychology, 95, 524–536.
37. Graesser, A. C., & Person, N. K. (1994). Question asking during tutoring. American Educational Research Journal, 31, 104–137.
38. Graesser, A. C., Person, N. K., & Magliano, J. P. (1995). Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied Cognitive Psychology, 9(359), 1–28.
39. Graesser, A. C., Wiemer-Hastings, K., Wiemer-Hastings, P., Kreuz, R., & the Tutoring Research Group. (1999). AutoTutor: A simulation of a human tutor. Journal of Cognitive Systems Research, 1, 35–51.
40. Graesser, A. C., Wiley, J., Goldman, S. R., O’Reilly, T., Jeon, M., & McDaniel, B. (2007). SEEK Web Tutor: Fostering a critical stance while exploring the causes of volcanic eruption. Metacognition and Learning, 2(2–3), 89–105.
41. Gratch, J., Rickel, J., Andre, E., Cassell, J., Petajan, E., & Badler, N. (2002). Creating interactive virtual humans: Some assembly required. IEEE Intelligent Systems, 17, 54–63.
42. Halpern, D. F. (2002). An introduction to critical thinking (4th ed.). Mahwah, NJ: Erlbaum.
43. Hayes, J. R., & Simon, H. A. (1977). Psychological differences among problem isomorphs. In J. Castellan, D. B. Pisoni, & G. Potts (Eds.), Cognitive theory (Vol. 2). Hillsdale, NJ: Erlbaum.
44. Hu, X., & Graesser, A. C. (2004). Human Use Regulatory Affairs Advisor (HURAA): Learning about research ethics with intelligent learning modules. Behavioral Research Methods, Instruments, and Computers, 36, 241–249.
45. Jackson, G. T., Olney, A., Graesser, A. C., & Kim, H. J. (2006). AutoTutor 3D simulations: Analyzing user’s actions and learning trends. In R. Son (Ed.), Proceedings of the 28th annual meetings of the Cognitive Science Society (pp. 1557–1562). Mahwah, NJ: Erlbaum.
46. Jackson, G. T., Ventura, M. J., Chewle, P., Graesser, A. C., & the Tutoring Research Group. (2004). The impact of Why/AutoTutor on learning and retention of conceptual physics. In J. C. Lester, R. M. Vicari, & F. Paraguacu (Eds.), Intelligent tutoring systems 2004 (pp. 501–510). Berlin: Springer.
47. Johnson, W. L., & Beal, C. (2005). Iterative evaluation of a large-scale intelligent game for language learning. In C. Looi, G. McCalla, B. Bredeweg, & J. Breuker (Eds.), Artificial intelligence in education: Supporting learning through intelligent and socially informed technology (pp. 290–297). Amsterdam: IOS Press.
48. Johnson, W. L., Rickel, J., & Lester, J. (2000). Animated pedagogical agents: Face-to-face interaction in interactive learning environments. International Journal of Artificial Intelligence in Education, 11, 47–78.
49. Jurafsky, D., & Martin, J. H. (2000). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, NJ: Prentice Hall.
50. King, A. (1994). Guiding knowledge construction in the classroom: Effects of teaching children how to question and how to explain. American Educational Research Journal, 31, 338–368.
51. Kolodner, J. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufman.
52. Landauer, T., McNamara, D. S., Dennis, S., & Kintsch, W. (Eds.). (2007). Handbook on latent semantic analysis. Mahwah, NJ: Erlbaum.
53. Lepper, M. R., & Henderlong, J. (2000). Turning “play” into “work” and “work” into “play”: 25 years of research on intrinsic versus extrinsic motivation. In C. Sansone & J. M. Harackiewicz (Eds.), Intrinsic and extrinsic motivation: The search for optimal motivation and performance (pp. 257–307). San Diego, CA: Academic Press.
54. Linn, M. C., Davis, E. A., & Bell, P. (Eds.). (2004). Internet environments for science education. Mahwah, NJ: Lawrence Erlbaum.
55. Maki, R. H. (1998). Test predictions over text material. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 117–144), Mahwah, NJ: Lawrence Erlbaum.
56. Malone, T. W., & Lepper, M. R. (1987). Making learning fun: A taxonomy of intrinsic motivations for learning. Cognitive Science, 5(4), 333–369.
57. Mayer, R. E. (2005). Multimedia learning. Cambridge, MA: Cambridge University Press.
58. McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38, 1–30.
59. McNamara, D. S., Levinstein, I. B., & Boonthum, C. (2004). iSTART: Interactive strategy trainer for active reading and thinking. Behavioral Research Methods, Instruments, and Computers, 36, 222–233.
60. McNamara, D. S., O’Reilly, T., Best, R., & Ozuru, Y. (2006). Improving adolescent students’ reading comprehension with iSTART. Journal of Educational Computing Research, 34, 147–171.
61. Moreno, R., & Mayer, R. E. (2004). Personalized messages that promote science learning in virtual environments. Journal of Educational Psychology, 96(1), 165–173.
62. Moreno, R., Mayer, R. E., Spires, H. A., & Lester, J. C. (2001). The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, 19, 177–213.
63. Norman, D. A. (1994). How might people interact with agents? Communication of the ACM, 37(7), 68–71.
64. Palincsar, A. S., & Brown, A. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1, 117–175.
65. Person, N. K., Graesser, A. C., & the Tutoring Research Group. (2002). Human or computer? AutoTutor in a bystander Turing test. In S. A. Cerri, G. Gouarderes, & F. Paraguacu (Eds.), Intelligent tutoring systems 2002 (pp. 821–830). Berlin: Springer.
66. Person, N. K., Kreuz, R. J., Zwaan, R., & Graesser, A. C. (1995). Pragmatics and pedagogy: Conversational rules and politeness strategies may inhibit effective tutoring. Cognition and Instruction, 13, 161–188.
67. Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, televisions, and new media like real people and places. Cambridge: Cambridge University Press.
68. Rosenshine, B., Meister, C., & Chapman, S. (1996). Teaching students to generate questions: A review of the intervention studies. Review of Educational Research, 66, 181–221.
69. Shneiderman, B., & Plaisant, C. (2005). Designing the user interface: Strategies for effective human–computer interaction (4th ed.). Reading, MA: Addison-Wesley.
70. Sweller, J., & Cooper, M. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59–89.
71. Taraban, R., Rynearson, K., & Stalcup, K. A. (2001). Time as a variable in learning on the World-Wide Web. Behavior Research Methods, 33(2), 217–225.
72. VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31(1), 3–62.
73. VanLehn, K., Lynch, C., Taylor, L., Weinstein, A., Shelby, R. H., Schulze, K. G., et al. (2002). Minimally invasive tutoring of complex physics problem solving. In S. A. Cerri, G. Gouarderes, & F. Paraguacu (Eds.), Intelligent tutoring systems, 2002, 6th International Conference (pp. 367–376). Berlin: Springer.
74. Vygotsky, L. S. (1978). Mind and society: The development of higher mental processes. Cambridge, MA: Harvard University Press.
75. Wiley, J. (2001). Supporting understanding through task and browser design. Proceedings of the 23rd annual conference of the Cognitive Science Society (pp. 1136–1143). Hillsdale, NJ: Lawrence Erlbaum.
76. Wisher, R. A., & Fletcher, J. D. (2004). The case for advanced distributed learning. Information & Security, 14, 17–25.