Abstract
Automation of pedagogical interventions in Model-tracing cognitive tutors (MTCT) strongly depends on chained paradigms like a proper modeling of the knowledge involved behind the student’s actions. Knowledge is tracked for inferring its degree of mastery that convey to a constructive learning process. In this paper is presented a methodology based on a probabilistic model for generating pedagogical interventions under a self-regulated environment. The foundations for developing it are explicitly detailed up to their implementation, passing through the modeling of the cognitive and meta-cognitive student knowledge. Probabilistic model is encoded in a Bayesian network topology that increases fidelity assessment by independently diagnosing degree of mastery of the relevant knowledge components and allowing a straightforward interpretation of the knowledge involved in a student’s actions. Moreover, it is also interwoven with other processes for inferring decisions that will influence in the way pedagogical interventions are generated and promoting a self-regulated behavior. Preliminary results to assess effectiveness of the proposed approach are also presented by implementing it in a MTCT called TITUS.
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Paquette, L., Lebeau, J.F., Beaulieu, G., Mayers, A.: Designing a knowledge representation approach for the generation of pedagogical interventions by MTTs. Int. J. Artif. Intell. Educ. 25, 118–156 (2015)
Aleven, V., Nkambou, R., Bourdeau, J., Mizoguchi, R.: Rule-based cognitive modeling for intelligent systems. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems, pp. 33–62. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14363-2_3
Aleven, V., McLaren, B., Roll, I., Koedinger, K.: Toward meta-cognitive tutoring: a model of help-seeking with a cognitive tutor. Int. J. Artif. Intell. Educ. 16, 101–130 (2016)
Wieber, F., Thürmer, J.L., Gollwitzer, P.M.: Promoting the translation of intentions into action by implementation of intentions: behavioral effects and physiological correlates. Front. Hum. Neurosci. 9, 395 (2015). https://doi.org/10.3389/fnhum.2015.00395
Long, Y., Aleven, V.: Active learners: redesigning an intelligent tutoring system to support self-regulated learning. In: Hernández-Leo, D., Ley, T., Klamma, R., Harrer, A. (eds.) EC-TEL 2013. LNCS, vol. 8095, pp. 490–495. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40814-4_44
Pelánek, R., Jarusek, P.: Student modeling based on problem solving time. Int. J. Artif. Intell. Educ. 25, 493–519 (2015)
Conati, C., Gertner, A., VanLehn, K.: Using Bayesian networks to manage uncertainty in student modeling. User Model. User-Adap. Inter. 12, 371–417 (2002)
VanLehn, K.: Intelligent tutoring systems for continuous, embedded assessment. In: Dwyer, C. (ed.) The Future of Assessment: Shaping Teaching and Learning, pp. 113–138. Lawrence Erlbaum Associates, New York (2008)
VanLehn, K., Burleson, W., Girard, S., Chavez-Echeagaray, M.E., Gonzalez-Sanchez, J., Hidalgo-Pontet, Y., Zhang, L.: The affective meta-tutoring project: lessons learned. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 84–93. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07221-0_11
Martinez Bastida, J.P., Chukhray, A.G.: An active diagnostic algorithm for a gyroscopic sensors unit. In: 2016 II International Young Scientists Forum on Applied Physics and Engineering (YSF), pp. 29–32, Kharkiv (2016). https://doi.org/10.1109/ysf.2016.7753793
Kulik, A.S.: Fault diagnosis in dynamic systems via signal-parametric approach. In: IFAC/IMACS Symposium of Fault Detection, Supervision and a Technical Process, SAFE PROCESS 1991, vol. 1, pp. 157–162, Baden-Baden (1991)
VanLehn, K., Burleson, W., Chavez Echeagaray, M.E., Christopherson, R., Gonzalez Sanchez, J., Hastings, J., Hidalgo Pontet Y., Zhang, L.: The affective meta-tutoring project: how to motivate students to use effective meta-cognitive strategies. In: Proceedings of the 19th International Conference on Computers in Education, ICCE 2011, Chian Mai, Thailand, pp. 128–130 (2011)
Aleven, V.: Rule-based cognitive modeling for intelligent systems. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems, pp. 33–62. (2010)
Russel, S., Norving, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, USA (2010). 1132 p.
Arroyo, I., Woolf, B.P., Burelson, W., Muldner, K., Rai, D., Tai, M.: A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. Int. J. Artif. Intell. Educ. 24, 387–426 (2014). https://doi.org/10.1007/s40593-014-0023-y
Walker, E., Rummel, N., Koedinger, K.R.: Adaptive intelligent support to improve peer tutoring in algebra. Int. J. Artif. Intell. Educ. 24, 33–61 (2014). https://doi.org/10.1007/s40593-013-0001-9
Dzikovska, M., Steinhauser, N., Farrow, E., Moore, J., Campbell, G.: BEETLE II: deep natural language understanding and automatic feedback generation for intelligent tutoring in basic electricity and electronics. Int. J. Artif. Intell. Educ. 24, 284–332 (2014). https://doi.org/10.1007/s40593-014-0017-9
Biswas, G., Segedy, J.R., Bunchongchit, K.: From design to implementation to practice a learning by teaching system: Betty’s brain. J. Artif. Intell. Educ. 26, 350–364 (2016). https://doi.org/10.1007/s40593-015-0057-9
Pelánek, R., Jarusek, P.: Student modeling based on problem solving times. J. Artif. Intell. Educ. 25, 493–519 (2015). https://doi.org/10.1007/s40593-015-0048-x
Shanabrook, D.H., Arroyo, I., Woolf, B.P., Burleson, W.: Visualization of student activity patterns within intelligent tutoring systems. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 46–51. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30950-2_6
Chi, M., VanLehn, K., Litman, D.: Do micro-level tutorial decisions matter: applying reinforcement learning to induce pedagogical tutorial tactics. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6094, pp. 224–234. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13388-6_27
Wang, Y., Heffernan, N.: Leveraging first response time into the knowledge tracing model. In: Proceedings of the International Conference on Educational Data Mining, pp. 176–179. (2012)
Chukhray, A.G.: Methodology for learning algorithms, monography. National Aerospace University, KhAI, Kharkiv, Ukraine, 336 p. ISBN 978–966-662-548-2 (2013)
Decision Systems Laboratory GeNIe 2.0. Decision Systems Laboratory, University of Pittsburg (2013). http://genie.sis.pitt.edu
Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: lessons learned. J. Learn. Sci. 4(2), 167–207 (1995)
Bessing, S.B., Gilbert, S.B., Ourada, S., Ritter, S.: Authoring model-tracing cognitive tutors. Int. J. Artif. Intell. Educ. 19, 189–210 (2009)
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Martínez Bastida, J.P., Havrykenko, O., Chukhray, A. (2018). Developing a Self-regulation Environment in an Open Learning Model with Higher Fidelity Assessment. In: Bassiliades, N., et al. Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2017. Communications in Computer and Information Science, vol 826. Springer, Cham. https://doi.org/10.1007/978-3-319-76168-8_6
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