Collaborative Tutoring Architecture : A Generic Case Based Reasoning Multi-Agent

By Mourad ENNAJI, Hadhoum BOUKACHOUR, Mustapha MACHKOUR

Abstract


This article is a response to the problem of learner desertion encountered by e-learning platforms. We propose to equip the Learning Management System with a generic intelligent tutoring module. This module is based on the combination of Case Based Reasoning (CBR) and Multi-Agent Systems (MAS). The main advantage of this generic module is to offer the learner individualized follow-up and to prevent dropping out of school. Personalized monitoring is carried out by combining machine tutoring and human tutoring. We describe how the combination of CBR and MAS allows the adaptation of the learning process according to the profile of the student.

 


Full Text:

PDF

References


A. Aamodt, E. Plaza. Case-Based Reasoning. Foundational Issues, Methodological Variations, and System Approaches”, AI Communications., 1994, 7(1), pp. 39-59

D. W. Aha, L. A. Breslow , H. Muñoz-Avila.. Conversational case-based reasoning. Applied Intelligence., 2001, 14(1), pp. 9-32.

J. R. Anderson, E. Skwarecki. The automated tutoring of introductory computer programming. Commun. ACM., 1986, 29( 9), pp. 842–849.

C. Angelaki, I. Mavroidis. Communication and social presence: the impact on adult learners’ emotions in distance learning. Eur.J. Open Distance E-learn., 2013, 16(1), pp. 78–93.

M. Arguedas, F. Xhafa, T. Daradoumis. An ontology about emotion awareness and affective feedback in elearning. In: International Conference on Intelligent Networking and Collaborative Systems., 2015, pp. 156–163, doi:10.1109/INCoS.2015.78..

E. Armengol, E. Plaza.. Lazy induction of descriptions for relational case-based learning. In European Conference on Machine Learning . Springer, Berlin, Heidelberg., 2001, pp. 13-24.

I. Arroyo, B. P. Woolf, W. Burelson, K. Muldner., D. Rai, M. Tai.. A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. Int. J. Artif. Intell.Educ, 2014, 24(4), pp. 387–426, doi:10.1007/s40593-014-0023-y.

A. Balakrishnan. On modeling the affective effect on learning.In: Multi-disciplinary Trends in Artificial Intelligence, 2011, pp. 225–235, doi:10.1007/978-3-642-25725-4_20.

K. Barker, V. K. Chaudhri, S. Y. Chaw, P. Clark, J. Fan, D. J. Israel, S. Mishra, B. W. Porter, P. Romero, D. Tecuci, and others. A Question-Answering System for AP Chemistry: Assessing KR&R Technologies.,” in KR, 2004, pp. 488–497.

R. Bergmann. Experience management: foundations, development methodology, and internet-based applications, 2002, Springer-Verlag.

F. Bertola, V. Patti. Ontology-based affective models to organize artworks in the social semantic web. Inf. Process. Manag., 2016, 52(1), pp. 139–162, doi:10.1016/j.ipm.2015.10.003.

B. Bloom. Taxonomy of Educational Objectives, Handbook I:The Cognitive Domain, 1984, 2nd edn. Addison Wesley, Reading.

B. Bourdeau, ,M. Grandbastien. Modeling Tutoring Knowledge J. Advances in Intelligent Tutoring Systems, Springer, Studies in Computational Intelligence, 2010, volume 308, pp.123-143,.

[B. P. Butz, M. Duarte, and S. M. Miller. An intelligent tutoring system for circuit analysis. IEEE Trans. Educ., 2006, 49(2), pp. 216–223.

M. Charikar, C. Chekuri, T. Feder, R. Motwani. Incremental clustering and dynamic information retrieval. SIAM Journal on Computing, 33(6), 2004, pp. 1417-1440.

H. Chen, Z. Wu. On case-based knowledge sharing in semantic web. 15th International Conference on Tools with Artificial Intelligence, 2006 pp. 200– 206.

M. Clemens. The art of complex problem solving, http://www.idiagram.com/CP/cpprocess.html, 2005.

P. Cohen, J. Kulik, C. Kulik. Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal, 1982, 2 (19), pp. 237-248.

A. Cordier, B. Fuchs, J. Lieber, A. Mille. Failure Analysis for Domain Knowledge Acquisition in a Knowledge-Intensive CBR System. In : Case-Based Reasoning Research and Development, Proceedings of the 7th International Conference on Case Based Reasoning, ICCBR 2007, Belfast, Northern Ireland, 2007, pp. 463-477.

P. J. Denning. Computer science: the discipline. in Anthony Ralston & David Hemmindinger (eds.), Encyclopedia of Computer Science, 2000, 32, no 1, pp. 9-23.

F. M. Donini, M. Lenzerini, D. Nardi, A. Schaerf. Reasoning in description logics. Principles of Knowledge representation, 1996, pp. 191-236.

M. C. Duffy, R. Azevedo. Motivation matters: interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Comput. Hum. Behav. 2015, 52, pp. 338–348. doi:10.1016/j.chb.2015.05.041.

H. H. Emurian, H. K. Holden, R. A. Abarbanel. Managing programmed instruction and collaborative peer tutoring in the classroom: Applications in teaching JavaTM. Comput. Human Behav., 2008, 24(2), pp. 576–614.

C. González, J. C Burguillo, M. Llamas, & R. Laza. Designing intelligent tutoring systems: A personalization strategy using case-based reasoning and multi-agent systems. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal,, 2013, 2(1), pp. 41-54.

K. Hafner. Software tutors offer help and customized hints”, https://www.nytimes.com/2004/09/16/technology/circuits/16tuto.html, 2004.

Y. Hayashi, J. Bourdeau, R. Mizoguchi. Using Ontological Engineering to Organize Learning/Instructional. Theories and Build a Theory-Aware Authoring System, IJAIED, 2009, 19 (2), pp. 211-252.

C. Hayes, P. Cunningham. Shaping a CBR View with XML in Case-Based Reasoning Research and Development. Proceedings of the Third International Conference on Case-Based Reasoning, 2000, pp. 468-481.

N. Jaques, C. Conati, J. Harley, R. Azevedo. Predicting Affect from Gaze Data During Interaction with an Intelligent Tutoring System. In: 12th International Conference, 2014, pp. 29–38, doi:10.1007/978-3-319-07221-04.

S. Jiménez, R. Juárez-Ramírez, V. H. Castillo, A. Noriega. Integrating affective learning into intelligent tutoring systems. Universal Access in the Information Society, 2018, 17(4), pp. 679-692.

R. John. Canvas LMS course design: design, build, and teach your very own online course using the powerful tools of the Canvas Learning Management System. Editor. 2014, PACKT Publishing.

O. Kwon and N. Sadeh. Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping. Decision Support Systems, 2004, 37(2), pp. 199–213.

D. E. Knuth. The Art of Computer Programming: Fundamental Algorithms, 1997, 3rd ed., Addison Wesley.

J. Kolodner. Case-based reasoning. 1993, San Mateo, CA: Morgan Kaufmann Publishers.

V. Krishnamoorthy, B. Appasamy, and C. Scaffidi. Using intelligent tutors to teach students how APIs are used for software engineering in practice, IEEE Trans. Educ., 2013, 56(3), pp. 355–363.

D. Leake, A. Kinley, D. Wilson. Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning. In : Proc. of the 15th International Joint Conference on Artificial Intelligence, 1997, pp. 246-251.

M. Lenz, B. Bartsch-Spörl, H. D. Burkhard, S. Wess. Case-based reasoning technology: from foundations to applications. Springer, 2003, volume 1400.

S. Li and Q. Yang. Active cbr: An agent system that integrates case based reasoning and active databases. Knowledge and Information Systems, 2004, 3(2), pp. 225–251.

H. C. K Lin, C. H. Wu, Y. P. Hsueh. The influence of using affective tutoring system in accounting remedial instruction on learning performance and usability. Comput. Hum. Behav., 2014, 41, pp. 514–522.

A. Montazemi and K. Gupta. An adaptive agent for case description in diagnostic cbr systems. Computers in Industry, 2004, 29(3), pp. 209–224.

E. Mousavinasab, N. R. Zarifsanaiey, S. Niakan Kalhori, M. Rakhshan, L. Keikha, M. Ghazi Saeedi. Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 2021, 29(1), 142-163.

M. Murr. How forensic tools recover digital evidence (data structures),” http://www.forensicblog.org/how-forensic-tools-recover-digital-evidence-data-structures/, 2007.

L. S. Myneni, N. H. Narayanan, S. Rebello, A. Rouinfar, and S. Pumtambekar, An interactive and intelligent learning system for physics education. IEEE Trans. Learn. Technol., 2013, 6(3), pp. 228–239.

M. Nentwig, E. Rahm . Incremental clustering on linked data. In IEEE International Conference on Data Mining Workshops (ICDMW), 2018, pp. 531-538.

R. Nkambou., J. Bourdeau, R. Mizoguchi. Advances in Intelligent Tutoring Systems. Springer, Heidelberg: Studies in Computational Intelligence, 2010, vol 308.

E. Plaza. Cases as terms: A feature term approach to the structured representation of cases. In International Conference on Case-Based Reasoning . Springer Berlin Heidelberg, 1995, pp. 265-276.

A. Ramírez-Noriega, R. Juárez-Ramírez, Y. Martínez-Ramírez. Evaluation module based on Bayesian networks to Intelligent Tutoring Systems. International Journal of Information Management, 2017, vol. 37, issue, part A, pp. 1488-1498.

L. Razzaq, J. Patvarczki, S. Almeida, M. Vartak, M. Feng, N. N. T. Heffernan, K. R. Koedinger. The ASSISTment Builder: Supporting the Life Cycle of Tutoring System Creation. IEEE Tr. on Learning Technologies, 2009, 2 (2), pp. 157-166.

U. D. C Rica., S. Pedro, M. D. Oca, C. Rica, C. The emotional intelligence, its importance in the learning process. Educacio’n, 2012,´36(1), pp. 1–24.

P. Richard, M. Gagnon, J. Fortuny, N. Leduc, M. Tessier-Baillargeon. Means of Choice for Interactive Management of Dynamic Geometry Problems Based on Instrumented Behaviour. In American Journal of Computational Mathematics, 2013, vol 03, pp. 41-51.

S. Suebnukarn and P. Haddawy. COMET: A collaborative tutoring system for medical problem-based learning. IEEE Intell. Syst., 2007, 22(4), pp. 70–77.

J. Tchetagni, R. Nkambou, J. Bourdeau. Explicit Reflection in Prolog-Tutor, IJAIED, 2007, 17(2), pp. 169-217.

M. Tsuei. Using synchronous peer tutoring system to promote elementary students’ learning in mathematics. Comput. Educ., 2012, 58(4), pp. 1171–1182.

B. Toussaint, V. Luengo, L. Vadcard, J. Tonetti. Apprentissage de la chirurgie orthopédique assisté par ordinateur : Le cas du Système Tutoriel Intelligent TELEOS. Field Actions Science Reports. The journal of field actions, 2014.

B. Vesin, M. Ivanović, A. KlašNja-MilićEvić, Z. Budimac. Protus 2.0: Ontology-based semantic recommendation in programming tutoring system. Expert Syst. Appl., 2012, 39(15), pp. 12229–12246.

D. Xu, S. S. Jaggars. Performance Gaps Between Online and Face-to-Face Courses: Differences Across Types of Students and Academic Subject Areas. The Journal of Higher Education, 2015, 85(5), pp. 633-659., doi:10.1353/jhe.2014.0028.

D. Wang, H. Han, Z. Zhan, J. Xu, Q. Liu, G. Ren. A problem solving oriented intelligent tutoring system to improve students' acquisition of basic computer skills. Published in Computer & Education, 2015, volume 81, pp. 102-112.

E. Wenger. Artificial Intelligence and Tutoring Systems. Los Altos, CA: Kaufman Publishers, 1987.

W. Wilke, R. Bergmann. Techniques and Knowledge Used for Adaptation During CaseBased Problem Solving. Tasks and Methods in Applied Artificial Intelligence, LNAI 1416, Springer-Verlag, 1998, pp. 497-505.

M. Wooldridge. An Introduction to Multi-Agent Systems, 2nd ed., Wiley, 2009.

B. P. Woolf. Building Intelligent Interactive Tutors. Morgan Kaufmann., 2009.






International Journal of Information Science and Technology (iJIST) – ISSN: 2550-5114