AutoTutor and family: A review of 17 years of natural language tutoring

International Journal of Artificial Intelligence in Education

Volumn 24, Issue 4, 2014, Pages 427-469

  Nye, Benjamin D ^a   Graesser, Arthur C ^a   Hu, Xiangen ^a  

^a UNIVERSITY OF MEMPHIS   (United States)

Author keywords

AutoTutor; Computer assisted learning; Discourse processes; Intelligent tutoring systems; Natural language processing; Pedagogical agents

Indexed keywords

COMPUTER AIDED INSTRUCTION; DEEP LEARNING; E-LEARNING; SCAFFOLDS; SEMANTICS; TEACHING;

AUTOTUTOR; COMPUTER ASSISTED LEARNING; COMPUTER LITERACY; CRITICAL THINKING; INTELLIGENT TUTORING SYSTEM; NATURAL LANGUAGES; PEDAGOGICAL AGENTS; SEMANTIC PROCESSING;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84908674444     PISSN: 15604292     EISSN: 15604306     Source Type: Journal    
DOI: 10.1007/s40593-014-0029-5     Document Type: Review

References (150)

1. Aleven, V.; Mclaren, B. M.; Sewall, J.; Koedinger, K. R. (2009). A new paradigm for intelligent tutoring systems: example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105-154.
2. Anderson, L. W.; Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing. New York: Longman.
3. Azevedo, R.; Johnson, A.; Chauncey, A.; Burkett, C. (2010). Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. In M. S. Khine I. M. Saleh (Eds.), New science of learning (pp. 225-247). New York: Springer.
4. Azevedo, R.; Landis, R. S.; Feyzi-Behnagh, R.; Duffy, M.; Trevors, G.; Harley, J. M.; Hossain, G. (2012). The effectiveness of pedagogical agents' prompting and feedback in facilitating co-adapted learning with MetaTutor. In S. A. Cerri B. Clancey (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2012 (pp. 212-221). Berlin: Springer.
5. Bloom, B. S. (1956). Taxonomy of educational objectives, handbook I: The cognitive domain. New York: David McKay Co, Inc.
6. Bloom, B. S. (1984). The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4-16.
7. Brophy, S.; Biswas, G.; Katzlberger, T.; Bransford, J.; Schwartz, D. (1999). Teachable agents: Combining insights from learning theory and computer science. In S. P. Lajoie M. Vivet (Eds.), Proceedings of Artificial Intelligence in Education (AIED) 1999 (pp. 21-28). Amsterdam: IOS Press.
8. Burton, R. R. (1977). Semantic grammar: an engineering technique for constructing natural language understanding systems. ACM SIGART Bulletin, 61, 26.
9. Cade, W. L.; Maass, J. K.; Hays, P.; Olney, A. M. (2014). Animated presentation of pictorial and concept map media in biology. In Intelligent tutoring systems (pp. 416-425). Berlin: Springer.
10. Cai, Z.; Forsyth, C.; Germany, M. L.; Graesser, A. C.; Millis, K. (2012). Accuracy of tracking student's natural language in OperationARIES!: A serious game for scientific methods. In S. A. Cerri B. Clancey (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2012 (pp. 629-630). Berlin: Springer.
11. Cai, Z.; Graesser, A. C.; Forsyth, C.; Burkett, C.; Millis, K.; Wallace, P.; Halpern, D.; Butler, H. (2011). Trialog in ARIES: User input assessment in an intelligent tutoring system. In W. Chen S. Li (Eds.), Proceedings of the 3rd IEEE International Conference on Intelligent Computing and Intelligent Systems (pp. 429-433). Guangzhou: IEEE Press.
12. Carbonell, J. R. (1970). AI in CAI: an artificial-intelligence approach to computer-assisted instruction. Man-machine Systems, IEEE Transactions on, 11(4), 190-202.
13. Chi, M. T.; De Leeuw, N.; Chiu, M. H.; LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439-477.
14. Chi, M. T.; Roy, M.; Hausmann, R. G. (2008). Observing tutorial dialogues collaboratively: insights about human tutoring effectiveness from vicarious learning. Cognitive Science, 32(2), 301-341.
15. Chi, M.; Jordan, P.; VanLehn, K. (2014). When is tutorial dialogue more effective than step-based tutoring? In S. Trausan-Matu, K. E. Boyer, M. Crosby, K. Panourgia (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2014. Berlin: Springer.
16. Cohen, P. A.; Kulik, J. A.; Kulik, C. L. C. (1982). Educational outcomes of tutoring: a meta-analysis of findings. American Educational Research Journal, 19(2), 237-248.
17. Craig, S. D.; Gholson, B.; Brittingham, J. K.; Williams, J. L.; Shubeck, K. T. (2012). Promoting vicarious learning of physics using deep questions with explanations. Computers Education, 58(4), 1042-1048.
18. Craig, S. D.; Sullins, J.; Witherspoon, A.; Gholson, B. (2006). The deep-level-reasoning-question effect: the role of dialogue and deep-level-reasoning questions during vicarious learning. Cognition and Instruction, 24(4), 565-591.
19. Deerwester, S.; Dumais, S. T.; Furnas, G. W.; Landauer, T. K.; Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407.
20. Dehn, D. M.; Van Mulken, S. (2000). The impact of animated interface agents: a review of empirical research. International Journal of Human-Computer Studies, 52(1), 1-22.
21. Dillenbourg, P.; Traum, D. (2006). Sharing solutions: persistence and grounding in multi-modal collaborative problem solving. The Journal of the Learning Sciences, 15, 121-151.
22. D'Mello, S.; Picard, R.; Graesser, A. (2007). Towards an affect-sensitive autotutor. IEEE Intelligent Systems, 22(4), 53-61.
23. D'Mello, S. K.; Dowell, N.; Graesser, A. C. (2011a). Does it really matter whether students' contributions are spoken versus typed in an intelligent tutoring system with natural language? Journal of Experimental Psychology: Applied, 17(1), 1-17.
24. D'Mello, S. K.; Graesser, A. C. (2010). Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-Adapted Interaction, 20, 147-187.
25. D'Mello, S. K.; Graesser, A. C. (2012a). AutoTutor and affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems, 2(4), 23:2-23:29.
26. D'Mello, S. K.; Graesser, A. C. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145-157.
27. D'Mello, S. K.; Hays, P.; Williams, C.; Cade, W.; Brown, J.; Olney, A. (2010a). Collaborative lecturing by human and computer tutors. In V. Aleven, J. Kay, J. Mostow (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2010 (pp. 178-187). Berlin: Springer.
28. D'Mello, S. K.; Lehman, B.; Graesser, A. (2011b). A motivationally supportive affect-sensitive AutoTutor. In R. Calvo S. D'Mello (Eds.), New perspectives on affect and learning technologies (pp. 113-126). New York: Springer.
29. D'Mello, S.; Lehman, B.; Pekrun, R.; Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153-170.
30. D'Mello, S. K.; Olney, A.; Person, N. (2010b). Mining collaborative patterns in tutorial dialogues. Journal of Educational Data Mining, 2(1), 1-37.
31. D'Mello, S.; Olney, A.; Williams, C.; Hays, P. (2012b). Gaze tutor: a gaze-reactive intelligent tutoring system. International Journal of Human-Computer Studies, 70(5), 377-398.
32. Dolan, B.; Quirk, C.; Brockett, C. (2004). Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources. In Proceedings of the 20th International Conference on Computational Linguistics (p. 350-357). Stroudsburg, PA: Association for Computational Linguistics.
33. Dzikovska, M. O.; Farrow, E.; Moore, J. D. (2013). In H. C. Lane, K. Yacef, J. Mostow, P. Pavlik (Eds.), Proceedings of Artificial Intelligence in Education (AIED) 2013 (pp. 279-288). Berlin: Springer.
34. Epstein, R.; Roberts, G.; Beber, G. (2009). Parsing the Turing test: Philosophical and methodological issues in the quest for the thinking computer. Berlin: Springer.
35. Falmagne, J. C.; Cosyn, E.; Doignon, J. P.; Thiéry, N. (2006). The assessment of knowledge, in theory and in practice. In R. Missaoui J. Schmidt (Eds.), Formal concept analysis (pp. 61-79). Berlin: Springer.
36. Freedman, R.; Haggin, N.; Nacheva, D.; Leahy, T.; Stilson, R. (2004). Using a domain-independent reactive planner to implement a medical dialogue system. In T. Bickmore (Ed.), AAAI fall symposium on systems for health communication (pp. 24-31). Menlo Park: AAAI Press.
37. Forsyth, C. M.; Pavlik, P.; Graesser, A. C.; Cai, Z.; Germany, M.; Millis, K.; Butler, H.; Dolan, R. (2012). In K. Yacef, O. Zaïane, H. Hershkovitz, M. Yudelson, J. Stamper (Eds.), Proceedings of the 5th International Conference on Educational Data Mining (pp. 172-175). Chania: International Educational Data Mining Society.
38. Gholson, B.; Witherspoon, A.; Morgan, B.; Brittingham, J. K.; Coles, R.; Graesser, A. C.; Craig, S. D. (2009). Exploring the deep-level reasoning questions effect during vicarious learning among eighth to eleventh graders in the domains of computer literacy and Newtonian physics. Instructional Science, 37(5), 487-493.
39. Glass, M. (1997). Some phenomena handled by the CIRCSIM-Tutor Version 3 input understander. In D. Dankel (Ed.), Proceedings of the Florida Artificial Intelligence Research Symposium (FLAIRS) 1997 (pp. 21-25). Menlo Park: AAAI Press.
40. Gavaldà, M.; Waibel, A. (1998). Growing semantic grammars. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, 1 (pp. 451-456). New Brunswick: Association for Computational Linguistics.
41. Graesser, A. C. (2009). Inaugural editorial for Journal of Educational Psychology. Journal of Educational Psychology, 101, 259-261.
42. Graesser, A. C. (2011). Learning, thinking, and emoting with discourse technologies. The American Psychologist, 66(8), 743-757.
43. Graesser, A. C.; Cai, Z.; Louwerse, M.; Daniel, F. (2006). Question Understanding Aid (QUAID): a web facility that helps survey methodologists improve the comprehensibility of questions. Public Opinion Quarterly, 70, 3-22.
44. Graesser, A. C.; Chipman, P.; Haynes, B. C.; Olney, A. (2005a). AutoTutor: an intelligent tutoring system with mixed-initiative dialogue. Education, IEEE Transactions on, 48(4), 612-618.
45. Graesser, A. C.; Conley, M. W.; Olney, A. M. (2012a). Intelligent tutoring systems. In S. Graham K. Harris (Eds.), APA Educational Psychology Handbook: Vol. 3. Applications to Learning and Teaching (pp. 451-473). Washington, DC: American Psychological Association.
46. Graesser, A. C.; D'Mello, S. K.; Hu, X.; Cai, Z.; Olney, A.; Morgan, B. (2012b). AutoTutor. In P. McCarthy C. Boonthum-Denecke (Eds.), Applied natural language processing: Identification, investigation, and resolution (pp. 169-187). Hershey, PA: IGI Global.
47. Graesser, A. C.; D'Mello, S.; Person, N. K. (2009). Metaknowledge in tutoring. In D. Hacker, J. Donlosky, A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 361-382). New York: Taylor Francis.
48. Graesser, A. C.; Jackson, G. T.; Mathews, E. C.; Mitchell, H. H.; Olney, A.; Ventura, M.; Tutoring Research Group. (2003a). Why/AutoTutor: A test of learning gains from a physics tutor with natural language dialog. In R. Alterman D. Hirsh (Eds.), Proceedings of the 25rd Annual Conference of the Cognitive Science Society (pp. 1-5). Boston: Cognitive Science Society.
49. Graesser, A. C.; Jeon, M.; Dufty, D. (2008). Agent technologies designed to facilitate interactive knowledge construction. Discourse Processes, 45(4-5), 298-322.
50. Graesser, A. C.; Jeon, M.; Yang, Y.; Cai, Z. (2007a). Discourse cohesion in text and tutorial dialogue. Information Design Journal, 15, 199-213.
51. Graesser, A. C.; Li, H.; Forsyth, C. (2014). Learning by communicating in natural language with conversational agents. Current Directions in Psychological Science. In press.
52. Graesser, A. C.; Lin, D.; D'Mello, S. (2010). Computer learning environments with agents that support deep comprehension and collaborative reasoning. In M. Banich D. Caccamise (Eds.), Generalization of Knowledge: Multidisciplinary Perspectives (pp. 201-223). New York: Psychology Press.
53. Graesser, A. C.; Lu, S.; Jackson, G. T.; Mitchell, H.; Ventura, M.; Olney, A.; Louwerse, M. M. (2004a). AutoTutor: a tutor with dialogue in natural language. Behavior Research Methods, Instruments, and Computers, 36, 180-193.
54. Graesser, A. C.; McNamara, D. S.; Louwerse, M. M.; Cai, Z. (2004b). Coh-Metrix: analysis of text on cohesion and language. Behavioral Research Methods, Instruments, and Computers, 36, 193-202.
55. Graesser, A. C.; McNamara, D. S.; VanLehn, K. (2005b). Scaffolding deep comprehension strategies through Point Query, AutoTutor, and iSTART. Educational Psychologist, 40(4), 225-234.
56. Graesser, A. C.; Moreno, K.; Marineau, J.; Adcock, A.; Olney, A.; Person, N. (2003b). 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 (AIED) 2003 (pp. 47-54). Amsterdam: IOS Press.
57. Graesser, A. C.; Penumatsa, P.; Ventura, M.; Cai, Z.; Hu, X. (2007b). Using LSA in AutoTutor: Learning through mixed initiative dialogue in natural language. In T. Landauer, D. McNamara, S. Dennis, W. Kintsch (Eds.), Handbook of latent semantic analysis (pp. 243-262). Mahwah: Erlbaum.
58. Graesser, A. C.; Person, N. K. (1994). Question asking during tutoring. American Educational Research Journal, 31, 104-137.
59. Graesser, A. C.; Person, N. K.; Magliano, J. P. (1995). Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied Cognitive Psychology, 9, 1-28.
60. Graesser, A. C.; VanLehn, K.; Rosé, C. P.; Jordan, P. W.; Harter, D. (2001b). Intelligent tutoring systems with conversational dialogue. AI Magazine, 22(4), 39-51.
61. Graesser, A.C.; Ventura, M.; Jackson, G.T.; Mueller, J.; Hu, X.; Person, N. (2003). The impact of conversational navigational guides on the learning, use, and perceptions of users of a web site. Proceedings of the AAAI Spring Symposium 2003 on Agent-mediated Knowledge Management. Menlo Park, CA: AAAI Press.
62. Graesser, A. C.; Wiemer-Hastings, K.; Wiemer-Hastings, P.; Kreuz, R. (1999). AutoTutor: a simulation of a human tutor. Cognitive Systems Research, 1(1), 35-51.
63. Graesser, A. C.; Wiemer-Hastings, P.; Wiemer-Hastings, K.; Harter, D.; Tutoring Research Group, Person, N. (2000). Using latent semantic analysis to evaluate the contributions of students in AutoTutor. Interactive Learning Environments, 8(2), 129-147.
64. Graesser, A. C.; Wiley, J.; Goldman, S. R.; O'Reilly, T.; Jeon, M.; McDaniel, B. (2007c). SEEK Web tutor: fostering a critical stance while exploring the causes of volcanic eruption. Metacognition and Learning, 2(2-3), 89-105.
65. Halpern, D. F.; Millis, K.; Graesser, A. C.; Butler, H.; Forsyth, C.; Cai, Z. (2012). Operation ARA: a computerized learning game that teaches critical thinking and scientific reasoning. Thinking Skills and Creativity, 7(2), 93-100.
66. Hestenes, D.; Wells, M.; Swackhamer, G. (1992). Force concept inventory. The Physics Teacher, 30, 141-158.
67. Hu, X.; Cai, Z.; Han, L.; Craig, S. D.; Wang, T.; Graesser, A. C. (2009). AutoTutor Lite. In V. Dimitrova, R. Mizoguchi, B. Du Boulay, A. C. Graesser (Eds.), Proceedings of Artificial Intelligence in Education (AIED) 2009 (p. 802). Amsterdam: IOS Press.
68. Hu, X.; Graesser, A. C. (2004). Human use regulatory affairs advisor (HURAA): learning about research ethics with intelligent learning modules. Behavior Research Methods, Instruments, Computers, 36(2), 241-249.
69. Hwang, G. J.; Tsai, C. C. (2011). Research trends in mobile and ubiquitous learning: a review of publications in selected journals from 2001 to 2010. British Journal of Educational Technology, 42(4), E65-E70.
70. Jackson, G. T.; Dempsey, K. B.; McNamara, D. S. (2010). The evolution of an automated reading strategy tutor: From the classroom to a game-enhanced automated system. In M. S. Khine I. M. Saleh (Eds.), New science of learning (pp. 283-306). New York: Springer.
71. Jackson, G. T.; Dempsey, K. B.; McNamara, D. S. (2011). Short and long term benefits of enjoyment and learning within a serious game. In G. Biswas, S. Bull, J. Kay, A. Mitrovic (Eds.), Proceedings of Artificial Intelligence in Education (AIED) 2011 (pp. 139-146). Berlin: Springer.
72. Jackson, G. T.; Graesser, A. C. (2006). Applications of human tutorial dialog in AutoTutor: an intelligent tutoring system. Revista Signos, 39, 31-48.
73. Jackson, G. T.; Graesser, A. C. (2007). Content matters: An investigation of feedback categories within an ITS. In R. Luckin, K. Koedinger, J. Greer (Eds.), Proceedings of Artificial Intelligence in Education (AIED) 2007 (pp. 127-134). Amsterdam: IOS Press.
74. Jackson, G. T.; Olney, A.; Graesser, A. C.; Kim, H. J. (2006). AutoTutor 3-D simulations: Analyzing user's actions and learning trends. In R. Son (Ed.), Proceedings of the 28th Annual Meeting of the Cognitive Science Society (pp. 1557-1562). Mahwah: Erlbaum.
75. 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.
76. 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.
77. Jordan, P. W.; Makatchev, M.; Pappuswamy, U.; VanLehn, K.; Albacete, P. L. (2006). A natural language tutorial dialogue system for physics. In G. Sutcliffe R. Goebel (Eds.), Proceedings of Florida Artificial Intelligence Research Society Conference (FLAIRS) 2006 (pp. 521-526). Menlo Park: AAAI Press.
78. Kim, H. J.; Graesser, A. C.; Jackson, G. T.; Olney, A.; Chipman, P. (2005). The effectiveness of computer simulations in a computer-based learning environment. In Proceedings for e-Learn 2005: World Conference on E-learning in Corporate, Government, Healthcare, and Higher Education (pp. 1362-1367). Vancouver: AACE.
79. Kim, N.; Evens, M.; Michael, J. A.; Rovick, A. A. (1989). CIRCSIM-TUTOR: An intelligent tutoring system for circulatory physiology. In H. Maurer (Ed.), Proceedings of Computer Assisted Learning, 360 (pp. 254-266). Berlin: Springer.
80. Klahr, D. (2002). Exploring science: The cognition and development of discovery processes. Cambridge: MIT Press.
81. Koedinger, K. R.; Aleven, V.; Heffernan, N.; McLaren, B.; Hockenberry, M. (2004). Opening the door to non-programmers: Authoring intelligent tutor behavior by demonstration. In J. Lester, R. Vicari, F. Paraguaçu (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2004 (pp. 162-174). Berlin: Springer.
82. 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 (The Educational Psychology Series, pp. 897-964). New York: Routledge.
83. Kopp, K. J.; Britt, M. A.; Millis, K.; Graesser, A. C. (2012). Improving the efficiency of dialogue in tutoring. Learning and Instruction, 22(5), 320-330.
84. Landauer, T. K.; Foltz, P. W.; Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259-284.
85. Langston, M. C.; Graesser, A. C. (1993). The "Point and Query" interface: exploring knowledge by asking questions. Journal of Educational Multimedia and Hypermedia, 2(4), 355-367.
86. 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.
87. Lehman, B.; D'Mello, S. K.; Strain, A.; Mills, C.; Gross, M.; Dobbins, A.; Wallace, P.; Millis, K.; Graesser, A. C. (2013). Inducing and tracking confusion with contradictions during complex learning. International Journal of Artificial Intelligence in Education, 22, 85-105.
88. Lepper, M. R.; Woolverton, M. (2002). The wisdom of practice: Lessons learned from the study of highly effective tutors. In J. Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. 135-158). Orlando: Academic Press.
89. Lester, J. C.; Towns, S. G.; Fitzgerald, P. J. (1998). Achieving affective impact: visual emotive communication in lifelike pedagogical agents. International Journal of Artificial Intelligence in Education, 10, 278-291.
90. Link, K. E.; Kreuz, R. J.; Graesser, A. C. (2001). Factors that influence the perception of feedback delivered by a pedagogical agent. International Journal of Speech Technology, 4(2), 145-153.
91. Long, Y.; Aleven, V. (2013). Skill diaries: Improve student learning in an intelligent tutoring system with periodic self-assessment. In H. C. Lane, K. Yacef, J. Mostow, P. Pavlik (Eds.), Proceedings of Artificial Intelligence in Education (AIED) 2013 (pp. 219-228). Berlin: Springer.
92. Lu, X.; Di Eugenio, B.; Kershaw, T. C.; Ohlsson, S.; Corrigan-Halpern, A. (2007). Expert vs. non-expert tutoring: Dialogue moves, interaction patterns and multi-utterance turns. In Computational Linguistics and Intelligent Text Processing (pp. 456-467). Berlin: Springer.
93. Magliano, J. P.; Todaro, S.; Millis, K.; Wiemer-Hastings, K.; Kim, H. J.; McNamara, D. S. (2005). Changes in reading strategies as a function of reading training: a comparison of live and computerized training. Journal of Educational Computing Research, 32(2), 185-208.
94. Massaro, D. W. (1998). Perceiving talking faces: From speech perception to a behavioral principle (Vol. 1). Cambridge: MIT Press.
95. McNamara, D. S.; Graesser, A. C.; McCarthy, P. M.; Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge: Cambridge University Press.
96. McNamara, D. S.; Magliano, J. P. (2009). Self-explanation and metacognition. In D. Hacker, J. Donlosky, A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 60-81). New York: Taylor Francis.
97. McNamara, D. S.; O'Reilly, T. P.; Best, R. M.; Ozuru, Y. (2006). Improving adolescent students' reading comprehension with iSTART. Journal of Educational Computing Research, 34(2), 147-171.
98. McNamara, D. S.; O'Reilly, T.; Rowe, M.; Boonthum, C.; Levinstein, I. B. (2007). iSTART: A web-based tutor that teaches self-explanation and metacognitive reading strategies. Reading comprehension strategies: Theories, interventions, and technologies, 397-421.
99. McNamara, D. S.; Raine, R.; Roscoe, R.; Crossley, S.; Jackson, G. T.; Dai, J.; Graesser, A. C. (2012). The Writing-Pal: Natural language algorithms to support intelligent tutoring on writing strategies. Applied natural language processing and content analysis: Identification, investigation, and resolution, (pp. 298-311). Hershey: IGI Global.
100. Metcalfe, J.; Kornell, N. (2005). A region or proximal of learning model of study time allocation. Journal of Memory and Language, 52, 463-477.
101. Merrill, D. C.; Reiser, B. J.; Ranney, M.; Trafton, J. G. (1992). Effective tutoring techniques: a comparison of human tutors and intelligent tutoring systems. The Journal of the Learning Sciences, 2(3), 277-305.
102. Millis, K.; Forsyth, C.; Butler, H.; Wallace, P.; Graesser, A.; Halpern, D. (2011). Operation ARIES!: A serious game for teaching scientific inquiry. In Serious games and edutainment applications (pp. 169-195). London: Springer.
103. Morrison, D.; Nye, B. D.; Hu, X. (2014). Where in the data stream are we? Analyzing the flow of text in dialogue-based systems for learning. In R. Sottilare, A. Graesser, X. Hu, B. Goldberg (Eds.), Design recommendations for intelligent tutoring systems: Instructional management (pp. 237-247). Orlando: U.S. Army Research Laboratory.
104. Nesbit, J. C.; Adesope, O. O. (2006). Learning with concept and knowledge maps: a meta-analysis. Review of Educational Research, 76(3), 413-448.
105. Nwana, H. S. (1990). Intelligent tutoring systems: an overview. Artificial Intelligence Review, 4(4), 251-277.
106. Nwana, H. S. (1996). Software agents: an overview. Knowledge Engineering Review, 11(3), 205-244.
107. Nye, B. D. (2013). Integrating GIFT and AutoTutor with Sharable Knowledge Objects (SKO). In R. A. Sottilare, H. K. Holden (Eds.), Proceedings of the Artificial Intelligence in Education (AIED) 2013 Workshop on the Generalized Intelligent Framework for Tutoring (GIFT), (pp. 54-61). CEUR.
108. Nye, B. D.; Graesser, A. C.; Hu, X. (2014a). Multimedia learning with intelligent tutoring systems. In R. Mayer (Ed.), Multimedia learning (3rd ed.; pp. 703-728). New York: Cambridge University Press.
109. Nye, B. D.; Hajeer, M.; Forsyth, C.; Samei, B.; Hu, X.; Millis, K. (2014). Exploring real-time student models based on natural-language tutoring sessions: A look at the relative importance of predictors. In Z. Pardos and J. Stamper (eds.), Educational Data Mining (EDM) 2014, (pp.253-256).
110. Olney, A. M. (2009). GnuTutor: An open source intelligent tutoring system. In V. Dimitrova, R. Mizoguchi, B. Du Boulay, A. C. Graesser (Eds.), Proceedings of Artificial Intelligence in Education (AIED) 2009 (p. 803). Amsterdam: IOS Press.
111. Olney, A.; D'Mello, S.; Person, N.; Cade, W.; Hayes, P.; Williams, C.; Lehman, B.; Graesser, A. C. (2012). Guru: A computer tutor that models expert human tutors. In S. A. Cerri B. Clancey (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2012 (pp. 256-261). Berlin: Springer.
112. Pashler, H.; Cepeda, J. T.; Wixted, J. T.; Rohrer, D. (2005). When does feedback facilitate learning of words? Journal of Experimental Psychology: Learning, Memory, Cognition, 31, 3-8.
113. Person, N. K.; Erkel, M.; Graesser, A. C.; the Tutoring Research Group. (2002). AutoTutor passes the bystander Turing test. In M. Driscoll T. C. Reeves (Eds.), Proceedings for E-Learning 2002: World Conference on E-Learning in Corporate, Government, Healthcare, Higher Education (pp. 778-782). Montreal: AACE.
114. Person, N. K.; Graesser, A. C.; Kreuz, R. J.; Pomeroy, V. (2003). Simulating human tutor dialog moves in AutoTutor. International Journal of Artificial Intelligence in Education, 12, 23-39.
115. Person, N. K.; Graesser, A. C.; Magliano, J. P.; Kreuz, R. J. (1994). Inferring what the student knows in one-to-one tutoring: the role of student questions and answers. Learning and Individual Differences, 6, 205-219.
116. 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.
117. Person, N. K.; Olney, A.; D'Mello, S. K.; Lehman, B. (2012). Interactive concept maps and learning outcomes in Guru. In G. Youngblood P. McCarthy (Eds.), Proceedings of the Florida Artificial Intelligence Research Symposium (FLAIRS) 2012 (pp. 456-461). Menlo Park: AAAI Press.
118. Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223-231.
119. Ritter, S.; Anderson, J. R.; Koedinger, K. R.; Corbett, A. (2007). Cognitive Tutor: applied research in mathematics education. Psychonomic Bulletin Review, 14(2), 249-255.
120. 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.
121. Roscoe, R. D.; McNamara, D. S. (2013). Writing pal: feasibility of an intelligent writing strategy tutor in the high school classroom. Journal of Educational Psychology, 105(4), 1010-1025.
122. 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.
123. Rouet, J. (2006). The skills of document use: From text comprehension to web-based learning. Mahwah: Erlbaum.
124. Rus, V.; Graesser, A. C. (2006). Deeper natural language processing for evaluating student answers in intelligent tutoring systems. Proceedings of the American Association of Artificial Intelligence. Menlo Park, CA: AAAI.
125. Rus, V.; Baggett, W.; Gire, E.; Franceschetti, D.; Conley, M.; Graesser, A.C. (2013a). Towards Learner Models based on Learning Progressions in DeepTutor. In Sottilare, R. (Eds.), Learner Models, (pp. 185-196) Army Research Lab.
126. Rus, V.; Banjade, R.; Lintean, M.; Niraula, N.; Stefanescu, D. (2013b). SEMILAR: A Semantic Similarity Toolkit for Assessing Students' Natural Language Inputs. In D'Mello, S. K.; Calvo, R. A.; Olney, A. (Eds.), Proceedings of Educational Data Mining 2013 (pp. 402-403).
127. Rus, V.; D'Mello, S. K.; Hu, X.; Graesser, A. C. (2013c). Recent Advances in conversational intelligent tutoring systems. AI Magazine, 34, 42-54.
128. Rus, V.; McCarthy, P. M.; McNamara, D. S.; Graesser, A. C. (2008). A study of textual entailment. International Journal on Artificial Intelligence Tools, 17(04), 659-685.
129. Rus, V.; McCarthy, P. M.; Graesser, A. C.; McNamara, D. S. (2009). Identification of sentence-to-sentence relations using a textual entailer. Research on Language and Computation, 7(2-4), 209-229.
130. Rus, V.; Stefanescu, D.; Baggett, W.; Niraula, N.; Franceschetti, D.; Graesser, A. C. (2014). Macro-adaptation in conversational intelligent tutoring matters. In S. Trausan-Matu, K. E. Boyer, M. Crosby, K. Panourgia (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2014 (pp. 242-247). Berlin: Springer.
131. Schwartz, D.; Bransford, D. (1998). A time for telling. Cognition and Instruction, 16(4), 475-522.
132. Self, J. (1990). Theoretical foundations for intelligent tutoring systems. Journal of Artificial Intelligence in Education, 1(4), 3-14.
133. Shaffer, D. W. (2006). How computer games help children learn. New York: Palgrave Macmillan.
134. Shaffer, D. W. Graesser, A. C. (2010). Using a quantitative model of participation in a community of practice to direct automated mentoring in an ill-defined domain. In C. Lynch, K. Ashley, T. Mitrovic, V. Dimitrova, N. Pinkwart, V. Aleven (Eds.), Proceedings of the 4th International Workshop on Intelligent Tutoring Systems and Ill-Defined Domains (pp. 61-68).
135. Shubeck, K.; Craig, S. D.; Hu, X.; Faghihi, U.; Levy, M.; Koch, R. (2012). Incorporating natural language tutoring into a virtual world for emergency response training. In P. M. McCarthy G. M. Youngblood (Eds.), Proceedings of Florida Artificial Intelligence Research Society (FLAIRS) 2012 (p. 573). Menlo Park: AAAI Press.
136. Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78, 153-189.
137. Song, K. S.; Hu, X.; Olney, A.; Graesser, A. C. (2004). A framework of synthesizing tutoring conversation capability with web-based distance education courseware. Computers Education, 42(4), 375-388.
138. Sottilare, R. A.; Goldberg, B. S.; Brawner, K. W.; Holden, H. K. (2012). A modular framework to support the authoring and assessment of adaptive computer-based tutoring systems (CBTS). In The Interservice/Industry Training, Simulation Education Conference (I/ITSEC) 2012 (1). Arlington, VA: National Training Systems Association.
139. Stevens, A. L.; Collins, A. (1977). The goal structure of a Socratic tutor. In J. Ketchel (Ed.), Proceedings of the ACM Conference 1977 (pp. 256-263). New York: ACM Press.
140. Susarla, S.; Adcock, A.; Van Eck, R.; Moreno, K.; Graesser, A. C. (2003). Development and evaluation of a lesson authoring tool for AutoTutor. In V. Aleven, U. Hoppe, J. Kay, R. Mizoguchi, H. Pain, F. Verdejo, K. Yacef (Eds.), AIED2003 Supplemental Proceedings (pp. 378-387). Sydney: University of Sydney School of Information Technologies.
141. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.
142. VanLehn, K.; Graesser, A. C.; Jackson, G. T.; Jordan, P.; Olney, A.; Rosé, C. P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31(1), 3-62.
143. VanLehn, K.; Siler, S.; Murray, C.; Yamauchi, T.; Baggett, W. B. (2003). Why do only some events cause learning during human tutoring? Cognition and Instruction, 21(3), 209-249.
144. VanLehn, K.; van de Sande, B.; Shelby, R.; Gershman, S. (2010). The Andes physics tutoring system: An experiment in freedom. In R. Nkambou, R. Mizoguchi, J. Bourdeau (Eds.), Advances in intelligent tutoring systems (pp. 421-443). Berlin: Springer.
145. Wang, N.; Johnson, W. L.; Mayer, R. E.; Rizzo, P.; Shaw, E.; Collins, H. (2008). The politeness effect: pedagogical agents and learning outcomes. International Journal of Human-Computer Studies, 66(2), 98-112.
146. Wolfe, C. R.; Fisher, C. R.; Reyna, V. F.; Hu, X. (2012). Improving internal consistency in conditional probability estimation with an intelligent tutoring system and web-based tutorials. International Journal of Internet Science, 7(1), 37-54.
147. Wolfe, C. R.; Widmer, C. L.; Reyna, V. F.; Hu, X.; Cedillos, E. M.; Fisher, C. R.; Weil, A. M. (2013). The development and analysis of tutorial dialogues in AutoTutor Lite. Behavior Research Methods, 45(3), 623-636.
148. Wood, D.; Wood, H. (1996). Vygotsky, tutoring and learning. Oxford Review of Education, 22(1), 5-16.
149. Woolf, B. P. (2009). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Burlington: Morgan Kaufmann.
150. Woolf, B. P.; Arroyo, I.; Muldner, K.; Burleson, W.; Cooper, D. G.; Dolan, R.; Christopherson, R. M. (2010). The effect of motivational learning companions on low achieving students and students with disabilities. In V. Aleven, J. Kay, J. Mostow (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2010 (pp. 327-337). Berlin: Springer.