Methodology for Developing an Intelligent Tutoring System Based on Marzano’s Taxonomy
The Mexican educational system faces diverse challenges related with the quality and coverage of education. The development of Intelligent Tutoring Systems (ITS) may help to solve some of them by helping teachers to customize their classes according to the performance of the students in online courses. In this work, we propose the adaptation of a functional ITS based on Bloom’s taxonomy called Sistema de Apoyo Generalizado para la Enseñanza Individualizada (SAGE), to measure student’s metacognition and their emotional response based on Marzano’s taxonomy. The students and the system will share the control over the advance in the course, so they can improve their metacognitive skills. The system will not allow students to get access to subjects not mastered yet. The interaction between the system and the student will be implemented through Natural Language Processing techniques, thus avoiding the use of sensors to evaluate student’s response. The teacher will evaluate student’s knowledge utilization, which is equivalent to the last cognitive level in Marzano’s taxonomy.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474719Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 SEP, "Diario Oficial de la Federación," 13 Diciembre 2013. (Online). Available: http://normatecainterna.sep.gob.mx/work/models/normateca/Resource/253/1/images/programa_sectorial_educacion_2013_2018.pdf. (Accessed 16 Mayo 2018).
 G. Fenza and F. Orciuoli, "Building Pedagogical Models by Formal Concept Analysis," in International Conference on Intelligent Tutoring Systems, 2016.
 M. Eagle, A. Corbett, J. Stamper, B. M. McLaren, A. Wagner, B. MacLaren and A. Mitchell, "Estimating Individual Differences for Student Modeling in Intelligent Tutors from Reading and Pretest Data," in International Conference on Intelligent Tutoring Systems, 2016.
 Y. Long and V. Aleven, "Mastery-Oriented Shared Student/System Control Over Problem Selection in a Linear Equation Tutor," in International Conference on Intelligent Tutoring Systems, 2016.
 G. Fenza, F. Orciuoli and D. G. Sampson, "Building Adaptive Tutoring Model using Artificial Neural Networks and Reinforcement Learning," in 2017 IEEE 17th International Conference on Advanced Learning Technologies, 2017.
 Dimitrova and P. Brna, "From Interactive Open Learner Modelling to Intelligent Mentoring: STyLE-OLM and Beyond," International Journal of Artificial Intelligence in Education, vol. 26, no. 1, pp. 332-349, 2016.
 R. Denaux, V. Dimitrova, L. Lau, P. Brna, D. Thakker and C. Steiner, "Employing linked data and dialogue for modelling cultural awareness of a user," in 19th International Conference on Intelligent User Interfaces, 2014.
 K. Goel and L. Polepeddi, "Jill Watson: A Virtual Teaching Assistant for Online Education," Georgia Institute of Technology, 2016.
 M. Taub and R. Azevedo, "Using Eye-Tracking to Determine the Impact of Prior Knowledge on Self-Regulated Learning with an Adaptive Hypermedia-Learning Environment," in International Conference on Intelligent Tutoring Systems, 2016.
 K. Vail, J. F. Grafsgaard, K. E. Boyer, E. N. Wiebe and J. C. Lester, "Predicting Learning from Student Affective Response to Tutor Questions," 2016.
 P. Pham and J. Wang, " Adaptive Review for Mobile MOOC Learning via Implicit Physiological Signal Sensing," in 18th ACM International Conference on Multimodal Interaction, 2016.
 Bloom, M. Engelhart, E. Furst, W. Hill and D. Krathwohl, Taxonomía de los objetivos de la educación. Clasificación de las metas educativas., vol. Tomo I. Ámbito del conocimiento., Marfil, 1970.
 M. Wixon, I. Arroyo, K. Muldner, W. Burleson, C. Lozano and B. Woolf, "The Opportunities and Limitations of Scaling Up Sensor-Free Affect Detection," in 7th International Conference on Educational Data Mining, 2014.
 R. Marzano and J. S. Kendall, The new taxonomy of educational objectives, CA: Corwin Press, 2006.
 J. Irvine, "A comparison of revised Bloom and Marzano’s New Taxonomy of Learning," Research in Higher Education Journal, vol. 33, 2017.
 E. Cambria, "Affective computing and sentiment analysis," IEEE Intelligent Systems, vol. 31, no. 2, pp. 102-107, 2016.
 M. A. Azim and M. H. Bhuiyan, "Text to Emotion Extraction Using Supervised Machine Learning Techniques," Telkomnika, vol. 16, no. 3, pp. 1394-1401, June 2018.
 M. Beutelspacher, A. l. Franzoni and Morales, A, "Sistema de apoyo generalizado para la enseñanza individualizada (SAGE)," B. S. Thesis, Instituto Tecnológico Autónomo de México, México D.F. 1995.