Intelligent tutoring systems

International Handbook of the Learning Sciences

Volumn , Issue , 2018, Pages 246-255

  Graesser, Arthur C ^a   Hu, Xiangen ^b,c   Sottilare, Robert ^d  

^a University of Memphis   (United States)

^b UNIVERSITY OF MEMPHIS   (United States)

^c CENTRAL CHINA NORMAL UNIVERSITY   (China)

^d U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85049273636     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9781315617572     Document Type: Chapter

References (56)

1. Aleven, V., McLaren, B. M., Roll, I., & Koedinger, K. R. (2016). Help helps, but only so much: Research on help seeking with intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 26(1), 205-223.
2. Aleven, V., McLaren, B. M., Sewall, J., & Koedinger, K. (2009). A new paradigm for intelligent tutoring sys-tems: Example-tracing tutors. International Journal of Artificial Intelligence in Education, 19, 105-154.
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. Arroyo, I., Muldner, K., Burleson, W., & Woolf, B. P. (2014). Adaptive interventions to address students’ negative activating and deactivating emotions during learning activities. In R. Sottilare, A. Graesser, X. Hu, & B. Goldberg (Eds.), Design recommendations for intelligent tutoring systems: Instructional management (Vol. 2, pp. 79-91). Orlando, FL: Army Research Laboratory.
5. Azevedo, R., Cromley, J. G., Moos, D. C., Greene, J. A., & Winters, F. I. (2011). Adaptive content and pro-cess scaffolding: A key to facilitating students’ self-regulated learning with hypermedia. Psychological Test and Assessment Modeling, 53, 106-140.
6. Baker, R. S. J. d., Corbett, A. T., Roll, I., & Koedinger, K. R. (2008) Developing a generalizable detector of when students game the system. User Modeling and User Adapted Interaction, 18(3), 287-314.
7. Baker, R. S. J. d., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010) Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223-241.
8. Baker, R. S. J. d., & Ocumpaugh, J. (2014) Interaction-based affect detection in educational software. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford Handbook of Affective Computing (pp. 233-245). Oxford, UK: Oxford University Press.
9. Beck, J. E., & Gong, Y. (2013). Wheel-spinning: Students who fail to master a skill. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education (pp. 431-440). Berlin: Springer.
10. Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology-Enhanced Learning, 5, 123-152.
11. Calvo, R. A., & D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1, 18-37.
12. Chi, M. T. H. (2009). Active-Constructive-Interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1, 73-105.
13. Chi, M. T. H., Siler, S., Yamauchi, T., Jeong, H., & Hausmann, R. (2001). Learning from human tutoring. Cognitive Science, 25, 471-534.
14. Clark, H. H. (1996). Using language. Cambridge, UK: Cambridge University Press.
15. Cohen, P. A., Kulik, J. A., & Kulik, C. C. (1982). Educational outcomes of tutoring: A meta-analysis of find-ings. American Educational Research Journal, 19, 237-248.
16. de Jong, T., Lazonder, A., Pedaste, M., & Zacharia, Z. (2018). Simulations, games, and modeling tools for learning. In F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences. New York: Routledge.
17. D’Mello, S. K. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learn-ing with technology. Journal of Educational Psychology, 105, 1082-1099.
18. D’Mello, S. K., & Graesser, A. C. (2012). AutoTutor and affective AutoTutor: Learning by talking with cogni-tively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems, 23, 1-38.
19. D’Mello, S., Olney, A. M., & Person, N. (2010). Mining collaborative patterns in tutorial dialogues. Journal of Educational Data Mining, 2(1), 1-37.
20. Dodds, P. V. W., & Fletcher, J. D. (2004). Opportunities for new “smart” learning environments enabled by next generation web capabilities. Journal of Education Multimedia and Hypermedia, 13, 391-404.
21. Dynarsky, M., Agodina, R., Heaviside, S., Novak, T., Carey, N., Campuzano, L., et al. (2007). Effectiveness of reading and mathematics software products: Findings from the first student cohort. Washington, DC: Institute of Education Sciences.
22. Dzikovska, M., Steinhauser, N., Farrow, E., Moore, J., & Campbell, G. (2014). BEETLE II: Deep natural language understanding and automatic feedback generation for intelligent tutoring in basic electricity and electronics. International Journal of Artificial Intelligence in Education, 24, 284-332.
23. Falmagne, J., Albert, D., Doble, C., Eppstein, D., & Hu, X. (2013). Knowledge spaces: Applications in education. Berlin-Heidelberg: Springer.
24. Fields, D. A., & Kafai, Y.B. (2018). Games in the learning sciences: reviewing Evidence from playing and mak-ing games for learning. In F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences (pp. 276-284). New York: Routledge.
25. Fischer, G. (2018) Massive open online courses (MOOCs) and rich landscapes of learning: a learning sciences perspective. In F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences (pp. 368-380). New York: Routledge.
26. Fletcher, J. D., & Morrison, J. E. (2012). DARPA Digital Tutor: Assessment data (IDA Document D-4686). Alexandria, VA: Institute for Defense Analyses.
27. Goldman, S. R., (2016). Cognition and metacognition [Webinar] In NAPLeS video series. Retrieved October 19, 2017, from http://isls-naples.psy.lmu.de/intro/all-webinars/goldman_video/index.html
28. Graesser, A.C. (2016). Conversations with AutoTutor help students learn. International Journal of Artificial Intelligence in Education, 26, 124-132.
29. Graesser, A. C., D’Mello, S. K., & Person, N., (2009). Meta-knowledge in tutoring. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 361-382). Mahwah, NJ: Erlbaum.
30. Graesser, A. C., Hu, X., Nye, B., & Sottilare, R. (2016). Intelligent tutoring systems, serious games, and the Generalized Intelligent Framework for Tutoring (GIFT). In H. F. O’Neil, E. L. Baker, and R. S. Perez (Eds.), Using games and simulation for teaching and assessment (pp. 58-79). Abingdon, UK: Routledge.
31. Graesser, A. C., Lippert, A. M., & Hampton, A. J. (2017). Successes and failures in building learning envi-ronments to promote deep learning: The value of conversational agents. In J. Buder & F. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 273-298). New York: Springer.
32. Jackson, G. T., & McNamara, D. S. (2013). Motivation and performance in a game-based intelligent tutoring system. Journal of Educational Psychology, 105, 1036-1049.
33. Johnson, W. L., & Lester, J. C. (2016). Twenty years of face-to-face interaction with pedagogical agents. International Journal of Artificial Intelligence in Education, 26(1), 25-36.
34. Johnson, W. L., & Valente, A. (2009). Tactical Language and Culture Training Systems: Using AI to teach foreign languages and cultures. AI Magazine, 30, 72-83.
35. Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757-798.
36. Kulik, J. A., & Fletcher, J. D. (2015). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 85, 171-204.
37. Looi, C.-K., & Wong, L.-H. (2018) Mobile computer-supported collaborative learning. In F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences (pp. 359-367). New York: Routledge.
38. Ma, W. Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analytic survey. Journal of Educational Psychology, 106, 901-918.
39. McQuiggan, S., & Lester, J. (2009). Modelling affect expression and recognition in an interactive learning envi-ronment. International Journal of Learning Technology, 4, 216-233.
40. Means, B., Peters, V., & Zheng, Y. (2014). Lessons from five years of funding digital courseware: Postsecondary success portfolio review, full report. Menlo Park, CA: SRI Education.
41. Mitrovic, A., Martin, B., & Suraweera, P. (2007). Intelligent tutors for all: The constraint-based approach. IEEE Intelligent Systems, 22, 38-45.
42. Nye, B. D., Graesser, A. C., & Hu, X. (2014). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24, 427-469.
43. Pomerance, L., Greenberg, J., & Walsh, K. (2015). Learning about learning: Do textbooks deliver on what every new teacher needs to know. National Council on Teacher Quality. Retrieved form www.nctq.org/dmsView/Learning_About_Learning_Report
44. Ritter, S., Anderson, J. R., Koedinger, K. R., & Corbett, A. (2007) Cognitive Tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14, 249-255.
45. Rus, V., Maharjan, N., & Banjade, R. (2015). Unsupervised discovery of tutorial dialogue modes in human-to-human tutorial data. Proceedings of the Third Annual GIFT Users Symposium (R. Sottilare & A. M. Sinatra, Eds.) (pp. 63-80), Army Research Lab, June 2015.
46. Siemens, G., Gasevic, D., & Dawson, S. (Eds.) (2015). Preparing for the digital university: A review of the history and current state of distance, blended, and online learning. Alberta, Canada: MOOC Research Initiative.
47. Sinclair, J. & Coulthart, M. (1975) Towards an analysis of discourse: The English used by teachers and pupils. London: Oxford University Press.
48. Sottilare, R. A., Goldberg, B. S., Brawner, K. W., & Holden, H. K. (2012). A modular framework to sup-port the authoring and assessment of adaptive computer-based tutoring systems (CBTS). In The Interservice/Industry Training, Simulation & Education Conference (I/ITSEC) (Vol. 2012, No. 1, pp. 1-13). Orlando, FL: National Training Systems Association.
49. Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of intelligent tutoring systems on K-12 students’ mathematical learning. Journal of Educational Psychology, 105, 971-987.
50. Steenbergen-Hu, S., & Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology, 106, 331-347.
51. VanLehn, K. (2006) The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227-265.
52. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems and other tutor-ing systems. Educational Psychologist, 46, 197-221.
53. VanLehn, K., Lynch, C., Schultz, 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.
54. Ward, W., Cole, R., Bolaños, D., Buchenroth-Martin, C., Svirsky, E., & Weston, T. (2013). My Science Tutor: A conversational multimedia virtual tutor. Journal of Educational Psychology, 105, 1115-1125.
55. Winne, P. H. (2011). Cognitive and metacognitive factors in self-regulated learning. In B. J. Zimmerman and D. H. Schunk (Eds.), Handbook of Self-Regulation of Learning and Performance (pp.15-32). New York: Routledge.
56. Woolf, B. P. (2009). Building intelligent tutoring systems. Burlington, MA: Morgan Kaufman.