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SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics

Nye, Benjamin D., Pavlik, Philip I., Windsor, Alistair, Olney, Andrew M., Hajeer, Mustafa, Hu, Xiangen·2018·International Journal of STEM Education
DOI 10.1186/s40594-018-0109-4
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Research works printed but not linked: 35

  1. Adcock, AB, & Eck, RNV (2005). Reliability and factor structure of the attitude toward tutoring agent scale (attas). Journal of Interactive Learning Research, 16(2), 195.
  2. Aleven, V, & Koedinger, K (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26(2), 147–179. · DOI · Paper page
  3. Aleven, V, Ogan, A, Popescu, O (2004). Evaluating the effectiveness of a tutorial dialogue system for self-explanation. In Intelligent Tutoring Systems (ITS) 2004. Springer, New York, (pp. 443–454).
  4. Aleven, V, McLaren, BM, Sewall, J, Koedinger, KR (2009). A new paradigm for intelligent tutoring systems: example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105–154.
  5. Atkinson, RK, Renkl, A, Merrill, MM (2003). Transitioning from studying examples to solving problems: effects of self-explanation prompts and fading worked-out steps. Journal of Educational Psychology, 95(4), 774. · DOI · Paper page
  6. Beck, JE, & Gong, Y (2013). Wheel-spinning: students who fail to master a skill. In International Conference on Artificial Intelligence in Education. Springer, Heidelberg, (pp. 431–440). · DOI
  7. Bloom, BS. (1956). Taxonomy of educational objectives: the classification of educational goals. Harlow: Longman Group.
  8. Chi, M, & VanLehn, K (2010). Meta-cognitive strategy instruction in intelligent tutoring systems: how, when, and why. Educational Technology and Society, 13(1), 25–39.
  9. Chi, MT (2009). Active-constructive-interactive: a conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1(1), 73–105. · DOI
  10. Craig, SD, Driscoll, DM, Gholson, B (2004). Constructing knowledge from dialog in an intelligent tutoring system: interactive learning, vicarious learning, and pedagogical agents. Journal of Educational Multimedia and Hypermedia, 13(2), 163.
  11. Craig, SD, Hu, X, Graesser, AC, Bargagliotti, AE, Sterbinsky, A, Cheney, KR, Okwumabua, T (2013). The impact of a technology-based mathematics after-school program using aleks on student’s knowledge and behaviors. Computers & Education, 68, 495–504. · DOI
  12. Falmagne, JC, Albert, D, Doble, C, Eppstein, D, Hu, X, (Eds.) (2013). Knowledge spaces. Berlin, Germany: Springer. · DOI
  13. Graesser, AC, Langston, MC, Lang, KL (1992). Designing educational software around questioning. Journal of Interactive Learning Research, 3(2), 235.
  14. Graesser, AC, D’Mello, SK, Hu, X, Cai, Z, Olney, A, Morgan, B (2012). AutoTutor. In: McCarthy, PM, & Boonthum, C (Eds.) In Applied natural language processing and content analysis: identification, investigation and resolution. IGI Global, Hershey, PA, (pp. 169–187). · DOI
  15. Heidig, S, & Clarebout, G (2011). Do pedagogical agents make a difference to student motivation and learning. Educational Research Review, 6(1), 27–54. · DOI
  16. Huang, X, Craig, SD, Xie, J, Graesser, A, Hu, X (2016). Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences, 47, 258–265. · DOI
  17. Landauer, TK, Foltz, PW, Laham, D (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259–284. · DOI · Paper page
  18. Lehman, B, D’Mello, S, Graesser, A (2012). Interventions to regulate confusion during learning. In Proceedings of the 11th international conference on Intelligent Tutoring Systems. Springer-Verlag, Berlin, Heidelberg, ITS’12, (pp. 576–578). · DOI
  19. Nye, BD (2016). Its, the end of the world as we know it: transitioning aied into a service-oriented ecosystem. International Journal of Artificial Intelligence in Education, 26(2), 756–770. · DOI
  20. Nye, BD, Graesser, AC, Hu, X (2013). Multimedia learning in intelligent tutoring systems. In: Mayer, RE (Ed.) In Multimedia Learning. 3rd Ed. Cambridge University Press, New York.
  21. Nye, BD, Graesser, AC, Hu, X (2014a). AutoTutor and family: a review of 17 years of science and math tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469. · DOI
  22. Nye, BD, Graesser, AC, Hu, X, Cai, Z (2014b). AutoTutor in the cloud: a service-oriented paradigm for an interoperable natural-language its. Journal of Advanced Distributed Learning Technology, 2(6), 35–48.
  23. Renkl, A (2005). The worked-out-example principle in multimedia learning. In: Mayer, RE (Ed.) In The Cambridge handbook of multimedia learning. Cambridge University Press, New York, (pp. 229–245). · DOI
  24. Roschelle, J, & Kaput, J (1996). Educational software architecture and systemic impact: the promise of component software. Journal of Educational Computing Research, 14(3), 217–228. · DOI
  25. Sabo, KE, Atkinson, RK, Barrus, AL, Joseph, SS, Perez, RS (2013). Searching for the two sigma advantage: evaluating algebra intelligent tutors. Computers in Human Behavior, 29(4), 1833–1840. · DOI
  26. Schommer-Aikins, M, Duell, OK, Hutter, R (2005). Epistemological beliefs, mathematical problem-solving beliefs, and academic performance of middle school students. The Elementary School Journal, 105(3), 289–304. · DOI
  27. Schroeder, NL, Adesope, OO, Gilbert, RB (2013). How effective are pedagogical agents for learning? A meta-analytic review. Journal of Educational Computing Research, 49(1), 1–39. · DOI
  28. Schwonke, R, Renkl, A, Krieg, C, Wittwer, J, Aleven, V, Salden, R (2009). The worked-example effect: not an artefact of lousy control conditions. Computers in Human Behavior, 25(2, SI), 258–266. · DOI
  29. Schworm, S, & Renkl, A (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology, 99(2), 285. · DOI
  30. Sullins, J, Meister, R, Craig, S, Wilson, W, Bargagliotti, A, Hu, X (2013). Is there a relationship between interacting with a mathematical intelligent tutoring system and students performance on standardized high-stake tests. Knowledge Spaces: Applications to education, 69–78. · DOI
  31. VanLehn, K (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227–265.
  32. VanLehn, K, Siler, S, Murray, C, Yamauchi, T, Baggett, WB (2003). Why do only some events cause learning during human tutoring?Cognition and Instruction, 21(3), 209–249. · DOI
  33. Vanlehn, K, Graesser, AC, Jackson, GT, Jordan, P, Olney, A, Rosé, CP (2007). When are tutorial dialogues more effective than reading?Cognitive Science, 31(1), 3–62. http://www.ncbi.nlm.nih.gov/pubmed/21635287 . · DOI · Paper page
  34. Venkatesh, V, Morris, MG, Davis, GB, Davis, FD (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478. · DOI · Paper page
  35. Willingham, DT. (2009). Why don’t students like school?: a cognitive scientist answers questions about how the mind works and what it means for the classroom. San Francisco: Wiley.

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Metadata

Authors
Nye, B. D., Pavlik, P. I., Windsor, A., Olney, A. M., Hajeer, M., & Hu, X.
Venue
International Journal of STEM Education
Publisher
Springer Science and Business Media LLC
Published
2018-04-13
Volume
5
Issue
1
ISSN
2196-7822
DOI
10.1186/s40594-018-0109-4
Cited by
55
References
5
Language
en
Licence
Funders
Office of Naval Research (N00014-12-C-0643)

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