Abstract
This paper presents a learning environment where a mining algorithm is used to learn patterns of interaction with the user and to represent these patterns in a scheme called item descriptors. The learning environment keeps theoretical information about subjects, as well as tools and exercises where the student can put into practice the knowledge obtained. One of the main purposes of the project is to stimulate colaborative learning through the interaction of students with different levels of knowledge. The students’ actions, as well as their interactions, are monitored by the system and used to find patterns that can guide the search for students that may play the role of a tutor. Such patterns are found with a particular learning algorithm and represented in item descriptors. The paper presents the educational application, the representation mechanism and learning algorithm used to mine social-affective data in order to create a recommendation model of tutors.
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References
Andrade, A., Jaques, P., Viccari, R., Bordini, R., Jung, J.: A Computational Model of Distance Learning Based on Vygotsky’s Socio-Cultural Approach. In: International Conference on Artificial Intelligence on Education Multi-Agent Based Learning Environments Workshop (2001)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Databases (1994)
Allison, L., Stern, L., Edgoose, T., Dix, T.I.: Sequence Complexity for Biological Sequence Analysis. Computers and Chemistry 24(1), 43–55 (2000)
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic Topicset Counting and Implication Rules for Market Basket Data. SIGMOD Record (ACM Special Interest Group on Management of Data) 26(2), 255 (1997)
Buchner, A., Mulvenna, M.: Discovering Behavioural Patterns in Internet Files. In: Intelligent Tutoring Systems 1998. LNCS, vol. 1452 (1998)
Claypool, M., Brown, D., Le, P., Waseda, M.: Inferring User Interest. IEEE Internet Computing 5(6), 32–39 (2001)
Echeita, G., Martin, E.: Interação Social e Aprendizagem. Artes Médicas, Porto Alegre (1995)
Nitzke, J., Carneiro, M.L., Franco, S.: Ambientes de Aprendizagem Cooperativa Apoiada pelo Computador e sua Epistemologia. Informatica na educação: Teoria & Prática 5(1), 13–23 (2002)
Gallant, S.: Connectionist Expert Systems. Communications of the ACM 31(2), 152–169 (1988)
Georgeff, M.P., Wallace, C.S.: A General Selection Criterion for Inductive Inference. In: European Conference on Artificial Intelligence, ECAI 1984, Pisa, pp. 473–482 (1984)
Gomes, E.R., Boff, E., Viccari, R.: Social, Affective and Pedagogical Agents for the Recommendation of Student Tutors. In: 7th Intelligent Tutoring Systems, Workshop on Social and Emotional Intelligence on Learning Environments (2004)
Gomes, E.R., Silveira, R.A., Viccari, R.M.: Utilização de agentes FIPA em ambientes para Ensino a Distância. In: XI Congresso Iberoamericano de Educação Superior em Computação, CIESC 2003 (2003)
Krulwich, B.: LIFESTYLE FINDER ( Intelligent User Profiling Using Large-Scale Demographic Data. Artificial Intelligence Magazine 18(2), 37–45 (1997)
Lin, W., Alvarez, S.A., Ruiz, C.: Collaborative Recommendation via Adaptive Association Rule Mining. In: Workshop on Web Mining for E-Commerce, KDD 2000 (2000)
Maturana, H.: Emoções e Linguagem na Educação e na Política. Ed. UFMG, Belo Horizonte (1998)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective Personalization Based on Association Rule Discovery from Web Usage Data. In: ACM Workshop on Web Information and Data Management (2001)
Pazzani, M.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999)
Piaget, J.: Estudos Sociológicos. Companhia Editora Forense, Rio de Janeiro (1973)
Pradhan, M., Provan, G.M., Middleton, B., Henrion, M.: Knowledge Engineering for Large Belief Networks. In: Uncertainty in Artificial Intelligence. Morgan Kaufmann (1994)
Reategui, E., Campbell, J.A., Torres, R.: AAAI - The Nineteenth National Conference on Artificial Intelligence, Workshop on Semantic Web Personalization (2004)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Topic-Based Collaborative Filtering Recommendation Algorithms. In: 10th International World Wide Web Conference, WWW 2010 (2001)
Sneath, P.A., Sokal, R.R.: Numerical Taxonomy – The Theory and Practice of Numerical Classification. Freeman, San Francisco (1973)
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Boff, E., Reategui, E. (2012). Mining Social-Affective Data to Recommend Student Tutors. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_68
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DOI: https://doi.org/10.1007/978-3-642-34654-5_68
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