Example-Tracing Tutors: Intelligent Tutor Development for Non-programmers

International Journal of Artificial Intelligence in Education

Volumn 26, Issue 1, 2016, Pages 224-269

  Aleven, Vincent ^a   McLaren, Bruce M ^a   Sewall, Jonathan ^a   Van Velsen, Martin ^a   Popescu, Octav ^a   Demi, Sandra ^a   Ringenberg, Michael ^a   Koedinger, Kenneth R ^a  

^a Carnegie Mellon University ^*  (United States)

Author keywords

Authoring tools; Example tracing tutors; Intelligent tutoring systems

Indexed keywords

COGNITIVE SYSTEMS; COMPUTER AIDED INSTRUCTION; COST EFFECTIVENESS; EDUCATION; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; PROBLEM SOLVING; STUDENTS;

AUTHORING TOOL; COLLABORATIVE LEARNING; EDUCATIONAL SETTINGS; EXAMPLE-TRACING TUTORS; INTELLIGENT TUTORING SYSTEM; INTELLIGENT TUTORS; LEARNING MANAGEMENT SYSTEM; PEDAGOGICAL APPROACH;

TEACHING;

EID: 84958976144     PISSN: 15604292     EISSN: 15604306     Source Type: Journal    
DOI: 10.1007/s40593-015-0088-2     Document Type: Article

References (142)

1. Adams, D. M.; McLaren, B. M.; Durkin, K.; Mayer, R. E.; Rittle-Johnson, B.; Isotani, S.; Velsen, M. V. (2014). Using erroneous examples to improve mathematics learning with a web-based tutoring system. Computers in Human Behavior, 36, 401-411. doi: 10.1016/j.chb.2014.03.053.
2. Adamson, D.; Dyke, G.; Jang, H.; Rosé, C. P. (2014). Towards an agile approach to adapting dynamic collaboration support to student needs. International Journal of Artificial Intelligence in Education, 24(1), 92-124. doi: 10.1007/s40593-013-0012-6.
3. Aleven, V. (2010). Rule-Based cognitive modeling for intelligent tutoring systems. In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds.), Studies in computational intelligence (Advances in intelligent tutoring systems, Vol. 308, pp. 33-62). Berlin: Springer. doi: 10.1007/978-3-642-14363-2-3.
4. Aleven, V. (2015). A is for Adaptivity, but what is Adaptivity? Re-defining the field of AIED. In K. Porayska-Pomsta, G. McCalla, & B. du Boulay (Eds.). Proceedings of the Workshops at the 17th International Conference on Artificial Intelligence in Education AIED 2015 (Vol. 4, Workshop on Les Contes du Mariage: Should AI stay married to Ed? A workshop examining the current and future identity of the AIED field). Available from: http://adenu.ia.uned.es/publications/aied2015ws/procs/v2/.
5. Aleven, V.; Koedinger, K. R. (2013). Knowledge component approaches to learner modeling. In R. Sottilare, A. Graesser, X. Hu, & H. Holden (Eds.), Design recommendations for adaptive intelligent tutoring systems (Learner modeling, Vol. I, pp. 165-182). Orlando: US Army Research Laboratory.
6. Aleven, V.; McLaren, B. M.; Roll, I.; Koedinger, K. R. (2006a). Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence in Education, 16, 101-128.
7. Aleven, V.; McLaren, B. M.; Sewall, J.; Koedinger, K. R. (2006b). The Cognitive Tutor Authoring Tools (CTAT): Preliminary evaluation of efficiency gains. In M. Ikeda, K. D. Ashley, & T. W. Chan (Eds.), Proceedings of the 8th International Conference on Intelligent Tutoring Systems, ITS 2006 (pp. 61-70). Berlin: Springer. doi: 10.1007/11774303-7.
8. Aleven, V.; Sewall, J.; McLaren, B. M.; Koedinger, K. R. (2006c). Rapid authoring of intelligent tutors for real-world and experimental use. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson, & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies, ICALT 2006 (pp. 847-851). Los Alamitos: IEEE Computer Society.
9. Aleven, V.; McLaren, B. M.; Sewall, J. (2009a). Scaling up programming by demonstration for intelligent tutoring systems development: an open-access web site for middle school mathematics learning. IEEE Transactions on Learning Technologies, 2(2), 64-78.
10. Aleven, V.; McLaren, B. M.; Sewall, J.; Koedinger, K. R. (2009b). A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105-154.
11. Aleven, V.; Beal, C. R.; Graesser, A. C. (2013). Introduction to the special issue on advanced learning technologies. Journal of Educational Psychology, 105(4), 929. doi: 10.1037/a0034155.
12. Aleven, V.; Sewall, J.; Popescu, O.; van Velsen, M.; Demi, S.; Leber, B. (2015a). Reflecting on twelve years of ITS authoring tools research with CTAT. In R. Sottilare, A. Graesser, X. Hu, & K. Brawner (Eds.), Design recommendations for adaptive intelligent tutoring systems (Authoring Tools, Vol. III, pp. 263-283). Orlando: US Army Research Laboratory.
13. Aleven, V.; Sewall, J.; Popescu, O.; Xhakaj, F.; Chand, D.; Baker, R. S.; Gasevic, D. (2015b). The beginning of a beautiful friendship? Intelligent tutoring systems and MOOCs. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on AI in Education, AIED 2015 (pp. 525-528). New York: Springer. doi: 10.1007/978-3-319-19773-9-53.
14. Aleven, V.; McLaughlin, E. A.; Glenn, R. A.; Koedinger, K. R. (forthcoming). Instruction based on adaptive learning technologies. In R. E. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction. Routledge.
15. Anderson, J. R.; Conrad, F. G.; Corbett, A. T. (1989). Skill acquisition and the LISP tutor. Cognitive Science, 13(4), 467-505. doi: 10.1016/0364-0213(89)90021-9.
16. Anderson, J. R.; Corbett, A. T.; Koedinger, K. R.; Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167-207.
17. Arroyo, I.; Beck, J.; Woolf, B. P.; Beal, C. R.; Schultz, K. (2000). Macroadapting animal watch to gender and cognitive differences with respect to hint interactivity and symbolism. In G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Proceedings of the 5th International Conference on Intelligent Tutoring Systems, ITS 2000 (pp. 574-583). Berlin: Springer Verlag.
18. Arroyo, I.; Woolf, B. P.; Burleson, W.; Muldner, K.; Rai, D.; Tai, M. (2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. International Journal of Artificial Intelligence in Education, 24(4), 387-426. doi: 10.1007/s40593-014-0023-y.
19. Atkinson, R. K.; Renkl, A.; Merrill, M. M. (2003). Transitioning from studying examples to solving problems: Effects of self-explanation prompts and fading worked-out steps. Journal of Educational Psychology, 95(4), 774-783.
20. Baghaei, N.; Mitrovic, A.; Irwin, W. (2007). Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams. International Journal of Computer-Supported Collaborative Learning, 2(2-3), 159-190. doi: 10.1007/s11412-007-9018-0.
21. Blessing, S. B.; Gilbert, S. (2008). Evaluating an authoring tool for model-tracing intelligent tutoring systems. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), ITS '08: Proceedings of the 9th International Conference on Intelligent Tutoring Systems (pp. 204-215). Berlin: Springer. doi: 10.1007/978-3-540-69132-7-25.
22. Blessing, S. B.; Gilbert, S. B.; Blankenship, L.; Sanghvi, B. (2009). From SDK to xPST: A new way to overlay a tutor on existing software. In Proceedings of the 2009 FLAIRS Conference (pp. 466-467). Association for the Advancement of Artificial Intelligence.
23. Blessing, S. B.; Gilbert, S. B.; Ourada, S.; Ritter, S. (2009b). Authoring model-tracing cognitive tutors. International Journal of Artificial Intelligence in Education, 19(2), 189-210.
24. Blessing, S.; Devasani, S.; Gilbert, S. (2011). Evaluation of WebxPST: A browser-based authoring tool for problem-specific tutors. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, AIED 2011 (pp. 423-425). Berlin: Springer.
25. Booth, J. L.; Lange, K. E.; Koedinger, K. R.; Newton, K. J. (2013). Using example problems to improve student learning in algebra: Differentiating between correct and incorrect examples. Learning and Instruction, 25, 24-34. doi: 10.1016/j.learninstruc.2012.11.002.
26. Borek, A.; McLaren, B. M.; Karabinos, M.; Yaron, D. (2009). How much assistance is helpful to students in discovery learning? In U. Cress, V. Dimitrova, & M. Specht (Eds.), Proceedings 4th European Conference on Technology-Enhanced Learning, EC-TEL 2009 (pp. 391-404). Berlin: Springer. doi: 10.1007/978-3-642-04636-0-38.
27. Bull, S.; Kay, J. (2010). Open learner models. In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds.), Studies in Computational Intelligence: Vol. 308. Advances in intelligent tutoring systems (pp. 301-322). Berlin, Heidelberg: Springer. doi: 10.1007/978-3-642-14363-2-15.
28. Chase, C.; Marks, J.; Bernett, D.; Bradley, M.; Aleven, V. (2015a). Towards the development of the invention coach: A naturalistic study of teacher guidance for an exploratory learning task. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education, AIED 2015 (pp. 558-561). New York: Springer. doi: 10.1007/978-3-319-19773-9-6.
29. Chase, C.; Marks, J.; Bernett, D.; Aleven, V. (2015b). The design of an exploratory learning environment to support Invention. Paper presented during the Workshop on Intelligent Support in Exploratory and Open-Ended Learning Environments, held as part of the 17th International Conference on Artificial Intelligence in Education, AIED 2015.
30. Clark, R. E.; Feldon, D.; van Merriënboer, J. J. G.; Yates, K.; Early, S. (2008). Cognitive task analysis. In J. M. Spector, M. D. Merrill, J. J. G. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed.; pp. 577-593). New York: Macmillan/Gale.
31. Conati, C.; Vanlehn, K. (2000). Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education, 11(4), 389-415.
32. Cook, R.; Kay, J.; Kummerfeld, B. (2015). MOOClm: User modelling for MOOCs. In F. Ricci, K. Bontcheva, O. Conlan, & S. Lawless (Eds.), Proceedings of the 23rd International Conference on User Modeling, Adaptation and Personalization, UMAP 2015 (pp. 80-91). Springer International Publishing. doi: 10.1007/978-3-319-20267-9-7.
33. Corbett, A. T.; Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253-278.
34. Corbett, A.; McLaughlin, M.; Scarpinatto, K. C. (2000). Modeling student knowledge: Cognitive Tutors in high school and college. User Modeling and User-Adapted Interaction, 10, 81-108.
35. Corbett, A.; Kauffman, L.; MacLaren, B.; Wagner, A.; Jones, E. (2010). A Cognitive Tutor for genetics problem solving: Learning gains and student modeling. Journal of Educational Computing Research, 42(2), 219-239.
36. de Baker, R. S. J.; Corbett, A. T.; Koedinger, K. R. (2007). The difficulty factors approach to the design of lessons in intelligent tutor curricula. International Journal of Artificial Intelligence in Education, 17(4), 341-369.
37. Devasani, S.; Gilbert, S. B.; Blessing, S. B. (2012). Evaluation of two intelligent tutoring system authoring tool paradigms: Graphical user interface-based and text-based. In Proceedings 21st Annual Conference on Behavior Representation in Modeling and Simulation 2012 (BRiMS 2012) (pp. 51-58). Red Hook, NY: Curran Associates, Inc.
38. Doroudi, S.; Holstein, K.; Aleven, V.; Brunskill, E. (2015). Towards understanding how to leverage sense-making, induction/refinement and fluency to improve robust learning. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, et al. (Eds.), Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015 (pp. 376-379). Worcester: International Educational Data Mining Society.
39. Earnshaw, Y. (2014). Effects of levels of instructional assistance on learning and mental effort in an intelligent tutoring system: Proportional reasoning and middle school students. Doctoral dissertation. Available from ProQuest Dissertations and Theses database. (UMI No. 3637974).
40. Forlizzi, J.; McLaren, B. M.; Ganoe, C.; McLaren, P. B.; Kihumba, G.; Lister, K. (2014). Decimal point: Designing and developing an educational game to teach decimals to middle school students. In C. Busch (Ed.), 8th European Conference on Games-Based Learning: ECGBL2014 (pp. 128-135). Reading: Academic Conferences and Publishing International.
41. Goguadze, G.; Sosnovsky, S.; Isotani, S.; McLaren, B. M. (2011). Evaluating a Bayesian student model of decimal misconceptions. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the 4th International Conference on Educational Data Mining, EDM 2011 (pp. 301-306). Worcester: International Data Mining Society.
42. Guan, C. Q.; Liu, Y.; Chan, D. H. L.; Ye, F.; Perfetti, C. A. (2011). Writing strengthens orthography and alphabetic-coding strengthens phonology in learning to read Chinese. Journal of Educational Psychology, 103(3), 509-522. doi: 10.1037/a0023730.
43. Harpstead, E.; MacLellan, C. J.; Aleven, V.; Myers, B. A. (2015). Replay analysis in open-ended educational games. In C. S. Loh, Y. Sheng, & D. Ifenthaler (Eds.), Serious games analytics (pp. 381-399). New York: Springer International Publishing. doi: 10.1007/978-3-319-05834-4-17.
44. Heffernan, N. T.; Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470-497. doi: 10.1007/s40593-014-0024-x.
45. Holmes, N. G.; Day, J.; Park, A. H.; Bonn, D. A.; Roll, I. (2014). Making the failure more productive: Scaffolding the invention process to improve inquiry behaviours and outcomes in productive failure activities. Instructional Science, 42(4), 523-538. doi: 10.1007/s11251-013-9300-7.
46. Janssen, J.; Bodemer, D. (2013). Coordinated computer-supported collaborative learning: Awareness and awareness tools. Educational Psychologist, 48(1), 40-55.
47. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509-539. doi: 10.1007/s10648-007-9054-3.
48. Kalyuga, S.; Ayres, P.; Chandler, P.; Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23-31. doi: 10.1207/S15326985EP3801-4.
49. Kay, J.; Reimann, P.; Diebold, E.; Kummerfeld, B. (2013). MOOCs: So many learners, so much potential. IEEE Intelligent Systems, 3, 70-77. doi: 10.1109/MIS.2013.66.
50. Kleij, F. M. V. D.; Feskens, R. C. W.; Eggen, T. J. H. M. (2015). Effects of feedback in a computer-based learning environment on students' learning outcomes. Review of Educational Research. doi: 10.3102/0034654314564881.
51. Kodaganallur, V.; Weitz, R.; Rosenthal, D. (2005). A comparison of model-tracing and constraint-based intelligent tutoring paradigms. International Journal of Artificial Intelligence in Education, 15(1), 117-144.
52. Kodavali, S. K.; Gilbert, S.; Blessing, S. (2010). Expansion of the xPST framework to enable non-programmers to create intelligent tutoring systems in 3D game environments. In V. Aleven, J. Kay, & J. Mostow (Eds.), Lecture Notes in Computer Science: Proceedings of the 10th International Conference on Intelligent Tutoring Systems, ITS 2010, vol. 2 (Vol. 6095, pp. 365-367). Berlin: Springer.
53. Koedinger, K. R. (2002). Toward evidence for instructional design principles: Examples from Cognitive Tutor Math 6. Invited paper. In Proceedings of PME-NA XXXIII (the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 21-49).
54. Koedinger, K. R.; Aleven, V. (2007). Exploring the assistance dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19(3), 239-264.
55. Koedinger, K. R.; Corbett, A. T. (2006). Cognitive Tutors: Technology bringing learning sciences to the classroom. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 61-78). New York: Cambridge University Press.
56. Koedinger, K.; Mitrovic, A. (2009). Authoring intelligent tutoring systems: Preface for special issue on authoring intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 19(2), 103-104.
57. Koedinger, K. R.; Terao, A. (2002). A cognitive task analysis of using pictures to support pre-algebraic reasoning. In W. Gray & C. D. Schunn (Eds.), Proceedings of the Twenty-fourth Annual Conference of the Cognitive Science Society (pp. 542-547). Taylor & Francis Group.
58. Koedinger, K. R.; Anderson, J. R.; Hadley, W. H.; Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8(1), 30-43.
59. Koedinger, K. R.; Suthers, D. D.; Forbus, K. D. (1999). Component-based construction of a science learning space. International Journal of Artificial Intelligence in Education, 10(3), 292-313.
60. 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. C. Lester, R. M. Vicario, & F. Paraguaçu (Eds.), Proceedings of seventh International Conference on Intelligent Tutoring Systems, ITS 2004 (pp. 162-174). Berlin: Springer.
61. Koedinger, K. R.; de Baker, R. S. J.; Cunningham, K.; Skogsholm, A.; Leber, B.; Stamper, J. (2010). A data repository for the EDM community: The PSLC datashop. In S. Ventura, C. Romero, M. Pechenizkiy, & R. S. J. D. Baker (Eds.), Handbook of educational data mining (pp. 43-55). Boca Raton: CRC Press.
62. Koedinger, K. R.; Corbett, A. T.; Perfetti, C. (2012). The knowledge-learning-instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757-798. doi: 10.1111/j.1551-6709.2012.01245.x.
63. Kumar, R.; Kim, J. (2014). Preface to the special issue on intelligent support for learning in groups. International Journal of Artificial Intelligence in Education, 24(1), 1-7. doi: 10.1007/s40593-013-0013-5.
64. Liew, C. W.; Xhakaj, F. (2015). Teaching a complex process: Insertion in red black trees. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education, AIED 2015 (pp. 698-701). New York: Springer International Publishing. doi: 10.1007/978-3-319-19773-9-95.
65. Liu, Y.; Wang, M.; Perfetti, C. A.; Brubaker, B.; Wu, S.; MacWhinney, B. (2011). Learning a tonal language by attending to the tone: an in vivo experiment. Language Learning, 61(4), 1119-1141. doi: 10.1111/j.1467-9922.2011.00673.x.
66. Long, Y.; Aleven, V. (2013a). Active learners: Redesigning an intelligent tutoring system to support self-regulated learning. In D. Hernández-Leo, T. Ley, R. Klamma, & A. Harrer (Eds.), Scaling up learning for sustained impact, Proceedings of the Eighth European Conference on Technology Enhanced Learning (EC-TEL 2013) (pp. 490-495). Berlin: Springer. doi: 10.1007/978-3-642-40814-4-44.ShortPaper.
67. Long, Y.; Aleven, V. (2013b). Supporting students' self-regulated learning with an open learner model in a linear equation tutor. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education, AIED 2013 (pp. 249-258). Berlin: Springer.
68. Long, Y.; Aleven, V. (2014). Gamification of joint student/system control over problem selection in a linear equation tutor. In S. Trausan-Matu, K. E. Boyer, M. Crosby, & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems, ITS 2014 (pp. 378-387). New York: Springer. doi: 10.1007/978-3-319-07221-0-47.
69. Lovett, M. C. (1998). Cognitive task analysis in the service of intelligent tutoring system design: A case study in statistics. In B. P. Goettle, H. M. Halff, C. L. Redfield, & V. J. Shute (Eds.), Intelligent Tutoring Systems, Proceedings of the Fourth International Conference, ITS 1998 (pp. 234-243). Berlin: Springer Verlag.
70. MacLellan, C.; Koedinger, K. R.; Matsuda, N. (2014). Authoring tutors with SimStudent: An evaluation of efficiency and model quality. In S. Trausan-Matu, K. E. Boyer, M. Crosby, & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems, ITS 2014 (pp. 551-560). Berlin: Springer. doi: 10.1007/978-3-319-07221-0-66.
71. Mathan, S. A.; Koedinger, K. R. (2005). Fostering the intelligent novice: Learning from errors with metacognitive tutoring. Educational Psychologist, 40(4), 257-265.
72. Matsuda, N.; Cohen, W. W.; Koedinger, K. R. (2015). Teaching the teacher: Tutoring SimStudent leads to more effective Cognitive Tutor authoring. International Journal of Artificial Intelligence in Education, 25(1), 1-34. doi: 10.1007/s40593-014-0020-1.
73. McKendree, J. (1990). Effective feedback content for tutoring complex skills. Human Computer Interaction, 5(4), 381-413. doi: 10.1207/s15327051hci0504-2.
74. McLaren, B. M.; Lim, S. J.; Koedinger, K. R. (2008). When and how often should worked examples be given to students? New results and a summary of the current state of research. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Meeting of the Cognitive Science Society (pp. 2176-2181). Austin: Cognitive Science Society.
75. McLaren, B. M.; DeLeeuw, K. E.; Mayer, R. E. (2011a). A politeness effect in learning with web-based intelligent tutors. International Journal of Human Computer Studies, 69(1-2), 70-79. doi: 10.1016/j.ijhcs.2010.09.001.
76. McLaren, B. M.; DeLeeuw, K. E.; Mayer, R. E. (2011b). Polite web-based intelligent tutors: Can they improve learning in classrooms? Computers & Education, 56(3), 574-584.
77. McLaren, B. M.; Adams, D.; Durkin, K.; Goguadze, G.; Mayer, R. E.; Rittle-Johnson, B.; Velsen, M. V. (2012). To err is human, to explain and correct is divine: A study of interactive erroneous examples with middle school math students. In A. Ravenscroft, S. Lindstaedt, C. Delgado Kloos, & D. Hernández-Leo (Eds.), 21st-Century Learning for 21st-Century Skills: 7th European Conference on Technology-Enhanced Learning, EC-TEL 2012 (pp. 222-235). Berlin: Springer. doi: 10.1007/978-3-642-33263-0-18.
78. McLaren, B. M.; van Gog, T.; Ganoe, C.; Yaron, D.; Karabinos, M. (2014). Exploring the assistance dilemma: Comparing instructional support in examples and problems. In S. Trausan-Matu, K. E. Boyer, M. Crosby, & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems, ITS 2014 (pp. 354-361). Berlin: Springer. doi: 10.1007/978-3-319-07221-0-66.
79. McLaren, B. M.; Adams, D. M.; Mayer, R. E. (2015a). Delayed learning effects with erroneous examples: a study of learning decimals with a web-based tutor. International Journal of Artificial Intelligence in Education, 25(4), 520-542.
80. McLaren, B. M.; van Gog, T.; Ganoe, C.; Yaron, D.; Karabinos, M. (2015b). Worked Examples are more efficient for learning than high-assistance instructional software. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on AI in Education, AIED 2015 (pp. 710-713). Berlin: Springer. doi: 10.1007/978-3-319-19773-9-98.
81. McLaren, B. M.; van Gog, T.; Ganoe, C.; Karabinos, M.; Yaron, D. (2016). The efficiency of worked examples compared to erroneous examples, tutored problem solving, and problem solving in classroom experiments. Computers in Human Behavior, 55, 87-99.
82. Mitrovic, A.; Ohlsson, S. (1999). Evaluation of a constraint-based tutor for a database language. International Journal of Artificial Intelligence in Education, 10(3-4), 238-256.
83. Mitrovic, A.; Martin, B.; Suraweera, P.; Zakharov, K.; Milik, N.; Holland, J.; Mcguigan, N. (2009). ASPIRE: An authoring system and deployment environment for constraint-based tutors. International Journal of Artificial Intelligence in Education, 19(2), 155-188.
84. Murray, T. (1999). Authoring intelligent tutoring systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education, 10(1), 98-129.
85. Murray, T.; Blessing, S.; Ainsworth, S. (2003). Authoring tools for advanced technology learning environments: Toward cost-effective adaptive, interactive and intelligent educational software. Amsterdam: Kluwer Academic Publishers.
86. Nathan, M. J. (1998). Knowledge and situational feedback in a learning environment for algebra story problem solving. Interactive Learning Environments, 5(1), 135-159.
87. Newell, A.; Simon, H. A. (1972). Human problem solving. Englewood Cliffs: Prentice-Hall.
88. Newell, A.; Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113-126. doi: 10.1145/360018.360022.
89. 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(4), 427-469. doi: 10.1007/s40593-014-0029-5.
90. Ogan, A.; Aleven, V.; Jones, C. (2009). Advancing development of intercultural competence through supporting predictions in narrative video. International Journal of Artificial Intelligence in Education, 19(3), 267-288.
91. Olsen, J. K.; Belenky, D. M.; Aleven, V.; Rummel, N. (2014a). Using an intelligent tutoring system to support collaborative as well as individual learning. In S. Trausan-Matu, K. E. Boyer, M. Crosby, & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems, ITS 2014 (pp. 134-143). Berlin: Springer. doi: 10.1007/978-3-319-07221-0-66.
92. Olsen, J. K.; Belenky, D. M.; Aleven, V.; Rummel, N.; Sewall, J.; Ringenberg, M. (2014b). Authoring tools for collaborative intelligent tutoring system environments. In S. Trausan-Matu, K. E. Boyer, M. Crosby, & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems, ITS 2014 (pp. 523-528). Berlin: Springer. doi: 10.1007/978-3-319-07221-0-66.
93. Olsen, J. K.; Rummel, N.; Aleven, V. (under review). Investigating effects of embedding collaboration in an intelligent tutoring system for elementary school students.
94. Paas, F. G. W. C.; Van Merriënboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86(1), 122-133.
95. Pane, J. F.; Griffin, B. A.; McCaffrey, D. F.; Karam, R. (2013). Effectiveness of Cognitive Tutor Algebra I at scale. Educational Evaluation and Policy Analysis, 0162373713507480. doi: 10.3102/0162373713507480.
96. Paquette, L.; Lebeau, J.-F.; Mayers, A. (2010). Authoring problem-solving tutors: A comparison between ASTUS and CTAT. In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds.), Advances in intelligent tutoring systems (pp. 377-405). Berlin: Springer. doi: 10.1007/978-3-642-14363-2-19.
97. Paquette, L.; Lebeau, J.-F.; Beaulieu, G.; Mayers, A. (2015). Designing a knowledge representation approach for the generation of pedagogical interventions by MTTs. International Journal of Artificial Intelligence in Education, 25(1), 118-156. doi: 10.1007/s40593-014-0030-z.
98. Pavlik, P. I.; Brawner, K.; Olney, A.; Mitrovic, A. (2013). A review of student models used in intelligent tutoring systems. In R. Sottilare, A. Graesser, X. Hu, & H. Holden (Eds.), Design recommendations for adaptive intelligent tutoring systems (Learner modeling, Vol. I, pp. 39-68). Orlando: US Army Research Laboratory.
99. Peterson, J.; Pardos, Z.; Rau, M. A.; Swigart, A.; Gerber, C.; McKinsey, J. (2015). Understanding student success in chemistry using gaze tracking and pupillometry. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on AI in Education, AIED 2015 (pp. 358-366). New York: Springer International Publishing. doi: 10.1007/978-3-319-19773-9-36.
100. Rau, M. A. (2015). Why do the rich get richer? A structural equation model to test how spatial skills affect learning with representations. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, et al. (Eds.), Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015 (pp. 350-359). Worcester: International Educational Data Mining Society.
101. Rau, M. A.; Wu, S. P. W. (2015). ITS support for conceptual and perceptual processes in learning with multiple graphical representations. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on AI in education, AIED 2015 (pp. 398-407). New York: Springer International Publishing. doi: 10.1007/978-3-319-19773-9-40.
102. Rau, M. A.; Aleven, V.; Rummel, N.; Rohrbach, S. (2012). Sense making alone doesn't do it: Fluency matters too! ITS support for robust learning with multiple representations. In S. A. Cerri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems, ITS 2012 (pp. 174-184). Berlin: Springer.
103. Rau, M.; Aleven, V.; Rummel, N. (2013). Interleaved practice in multi-dimensional learning tasks: Which dimension should we interleave?. Learning and Instruction, 23, 98-114. doi:learninstruc.2012.07.003.
104. Rau, M. A.; Aleven, V.; Rummel, N.; Pardos, Z. (2014). How should intelligent tutoring systems sequence multiple graphical representations of fractions? A multi-methods study. International Journal of Artificial Intelligence in Education, 24(1), 125-161. doi: 10.1007/s40593-013-0011-7.
105. Rau, M. A.; Aleven, V.; Rummel, N. (2015a). Successful learning with multiple graphical representations and self-explanation prompts. Journal of Educational Psychology, 107(1), 30-46. doi: 10.1037/a0037211.
106. Rau, M. A.; Michaelis, J. E.; Fay, N. (2015b). Connection making between multiple graphical representations: A multi-methods approach for domain-specific grounding of an intelligent tutoring system for chemistry. Computers & Education, 82, 460-485. doi: 10.1016/j.compedu.2014.12.009.
107. Razzaq, L.; Patvarczki, J.; Almeida, S. F.; Vartak, M.; Feng, M.; Heffernan, N. T.; Koedinger, K. R. (2009). The Assistment Builder: Supporting the life cycle of tutoring system content creation. IEEE Transactions on Learning Technologies, 2(2), 157-166.
108. Renkl, A. (2013). Toward an instructionally oriented theory of example-based learning. Cognitive Science, 38(1), 1-37. doi: 10.1111/cogs.12086.
109. Renkl, A.; Atkinson, R. K.; Grosse, C. S. (2003). How fading worked solution steps works - a cognitive load perspective. Instructional Science, 32, 1-24.
110. Rice, W. (2011). Moodle 2.0 e-learning course development: A complete guide to successful learning using Moodle. Birmingham: Packt Publishing Ltd.
111. Ritter, S.; Koedinger, K. R. (1996). An architecture for plug-in tutor agents. International Journal of Artificial Intelligence in Education, 7(3-4), 315-347.
112. 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.
113. Rittle-Johnson, B.; Koedinger, K. R. (2005). Designing knowledge scaffolds to support mathematical problem solving. Cognition and Instruction, 23(3), 313-349.
114. Roll, I.; Aleven, V.; Koedinger, K. R. (2010). The Invention Lab: Using a hybrid of model tracing and constraint-based modeling to offer intelligent support in inquiry environments. In V. Aleven, J. Kay, & J. Mostow (Eds.), Lecture Notes in Computer Science: Proceedings of the 10th International Conference on Intelligent Tutoring Systems, ITS 2010 (Vol. I, pp. 115-124). Berlin: Springer.
115. Roll, I.; Aleven, V.; McLaren, B. M.; Koedinger, K. R. (2011). Metacognitive practice makes perfect: Improving students' self-assessment skills with an intelligent tutoring system. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of the 15th international conference on artificial intelligence in education, AIED 2011 (pp. 288-295). Berlin: Springer. doi: 10.1007/978-3-642-21869-9-38.
116. Rummel, N.; Walker, E.; Aleven, V. (under review). Different futures of adaptive collaborative learning support. Manuscript submitted for publication.
117. Salden, R. J. C. M.; Aleven, V.; Schwonke, R.; Renkl, A. (2010a). The expertise reversal effect and worked examples in tutored problem solving. Instructional Science, 38(3), 289-307. doi: 10.1007/s11251-009-9107-8.
118. Salden, R. J. C. M.; Koedinger, K. R.; Renkl, A.; Aleven, V.; McLaren, B. M. (2010b). Accounting for beneficial effects of worked examples in tutored problem solving. Educational Psychology Review, 22(4), 379-392. doi: 10.1007/s10648-010-9143-6.
119. Schwartz, D. L.; Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129-184.
120. Schwartz, D. L.; Chase, C. C.; Oppezzo, M. A.; Chin, D. B. (2011). Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. Journal of Educational Psychology, 103(4), 759-775. doi: 10.1037/a0025140.
121. Slavin, R. E. (1996). Research on cooperative learning and achievement: What we know, what we need to know. Contemporary Educational Psychology, 21(1), 43-69.
122. Sottilare, R. (2012). Considerations in the development of an ontology for a generalized intelligent framework for tutoring. In I3M defense and homeland security simulation Conference (DHSS 2012).
123. Sottilare, R.; Graesser, A.; Hu, X.; Holden, H. (Eds.) (2013). Design recommendations for adaptive intelligent tutoring systems (Vol. I, Learner Modeling). Orlando: US Army Research Laboratory.
124. Sottilare, R.; Graesser, A.; Hu, X.; Holden, H. (Eds.) (2014). Design recommendations for adaptive intelligent tutoring systems (Vol. II - Instructional Management). Orlando: US Army Research Laboratory.
125. Sottilare, R.; Graesser, A.; Hu, X.; Brawner, K. (Eds.) (2015). Design recommendations for adaptive intelligent tutoring systems (Vol. III - Authoring Tools and Expert Modeling Techniques). Orlando: US Army Research Laboratory.
126. Stampfer, E.; Koedinger, K. R. (2013). When seeing isn't believing: Influences of prior conceptions and misconceptions. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 916-919). Berlin: Springer. doi: 10.1007/978-3-642-39112-5-145.
127. Stankov, S.; Rosić, M.; Žitko, B.; Grubišić, A. (2007). TEx-Sys model for building intelligent tutoring systems. Computers & Education, 51(3), 1017-1036. doi: 10.1016/j.compedu.2007.10.002.
128. Van Gog, T.; Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review, 22(2), 155-174. doi: 10.1007/s10648-010-9134-7.
129. VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227-265.
130. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.
131. VanLehn, K. (this issue). Regulative loops, step loops and task loops. International Journal of Artificial Intelligence in Education, 26(1).
132. Waalkens, M.; Aleven, V.; Taatgen, N. (2013). Does supporting multiple student strategies lead to greater learning and motivation? Investigating a source of complexity in the architecture of intelligent tutoring systems. Computers & Education, 60(1), 159-171. doi: 10.1016/j.compedu.2012.07.016.
133. Walker, E.; Rummel, N.; Koedinger, K. R. (2014). Adaptive intelligent support to improve peer tutoring in algebra. International Journal of Artificial Intelligence in Education, 24(1), 33-61. doi: 10.1007/s40593-013-0001-9.
134. Weiss, M. A. (2010). Data structures and problem solving using Java (4th Edition). New York: Pearson Education, Inc.
135. Wiese, E. S.; Koedinger, K.R. (2015). Grounded feedback in a fraction addition tutor. Paper presented as part of the Symposium Multiple Representations and Multimedia: Student Learning and Instruction at the 2015 Annual Meeting of the American Educational Research Association (AERA). Chicago, IL.
136. Woolf, B. P. (2009). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Burlington: Morgan Kaufmann.
137. Wylie, R.; Koedinger, K. R.; Mitamura, T. (2009). Is self-explanation always better? The effects of adding self-explanation prompts to an English grammar tutor. In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 1300-1305). Austin: Cognitive Science Society.
138. Wylie, R.; Koedinger, K. R.; Mitamura, T. (2010a). Analogies, explanations, and practice: Examining how task types affect second language grammar learning. In V. Aleven, J. Kay, & J. Mostow (Eds.), Lecture notes in computer science: Proceedings of the 10th International Conference on Intelligent Tutoring Systems, ITS 2010 (Vol. 6094, pp. 214-223). Berlin: Springer. doi: 10.1007/978-3-642-13388-6-26.
139. Wylie, R.; Koedinger, K. R.; Mitamura, T. (2010b). Extending the self-explanation effect to second language grammar learning. In K. Gomez, L. Lyons, & J. Radinsky (Eds.), Learning in the disciplines: Proceedings of the 9th International Conference of the Learning Sciences, ICLS 2010 (Vol. 1, pp. 57-64). Chicago: International Society of the Learning Sciences.
140. Wylie, R.; Sheng, M.; Mitamura, T.; Koedinger, K. (2011). Effects of adaptive prompted self-explanation on robust learning of second language grammar. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Lecture notes in computer science: Artificial intelligence in education (Vol. 6738, pp. 588-590). Berlin: Springer. doi: 10.1007/978-3-642-21869-9-110.
141. Xhakaj, F. (2015). Intelligent tutors and granularity a new approach to red black trees. Unpublished senior thesis, Department of Computer Science, Lafayette College, Easton Pennsylvania.USA.
142. Xhakaj, F.; Liew, C. W. (2015). A new approach to teaching red black tree. In V. Dagienė, C. Schulte, & T. Jevsikova (Eds.), Proceedings of the 20th ACM Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '15 (pp. 278-283). New York: ACM. doi: 10.1145/2729094.2742624.