Використання нечіткої логіки у процесі експертного оцінювання електронних навчальних ресурсів

The paper deals with expert evaluation of eLearning resources based on the theory of fuzzy logic using the method of hierarchy analysis. The concept of fuzzy logic was used to quantify qualitative data in real decision-making tasks. The authors propose to develop a recommender system based on fuzzy logic methods for expert evaluation of eLearning resources, including computer mathematics systems and for deciding on the selection of the most effective ones for use in the educational process. In the course of research the concept of recommender systems is considered and analyzed according to the field of application. The concept of the recommender system was introduced to support decision-making on the selection of the most effective eLearning resources. A literature review on expert evaluation, on using of fuzzy logic methods and recommender systems in the decision-making process is presented. The general structure of the recommender system of decision support with the description of all subsystems is given as well. The basic information of the theory of fuzzy logic concerning the decision-making process is described. The practical use of fuzzy logic theory in the process of choosing computer mathematics systems is considered. The main criteria for evaluating computer mathematics systems are given. The method of pairwise comparisons was used to calculate the importance of the criteria. The process of evaluating eLearning resources using fuzzy logic methods is described in detail and the algorithm of this approach is given. As a result of the expert assessment, a list of recommended alternatives to eLearning resources that meet the set criteria was obtained. The general structure of the recommender system of decision support for the selection of eLearning resources is given.Розглянуто процедури експертного оцінювання для визначення якості електронних навчальних ресурсів, що базуються на теорії нечіткої логіки та використанні методу аналізу ієрархій. Концепцію нечіткої логіки використано для кількісної оцінки якісних даних для генерування рекомендацій. Запропоновано проєкт рекомендаційної системи на підставі методів нечіткої логіки для експертного оцінювання електронних навчальних ресурсів, зокрема систем комп'ютерної математики та генерування рекомендацій щодо вибору найефективніших для використання в навчальному процесі. Розглянуто та проаналізовано поняття рекомендаційних систем залежно від сфери застосування. Введено поняття рекомендаційної системи для вибору найефективніших електронних навчальних ресурсів. Здійснено огляд наукових публікацій щодо застосування експертного оцінювання, використання методів нечіткої логіки та рекомендаційних систем. Наведено загальну архітектуру рекомендаційної системи з описом функціоналу підсистем. Показано основні можливості застосування теорії нечіткої логіки у процесах генерації рекомендацій. Розглянуто приклади практичного використання теорії нечіткої логіки в процесі вибору систем комп'ютерної математики. Наведено основні критерії оцінювання систем комп'ютерної математики. Використано метод парних порівнянь для розрахунку важливості критеріїв. Детально описано процес оцінювання електронних навчальних ресурсів з використанням методів нечіткої логіки та подано алгоритм роботи цього підходу. Внаслідок проведеного експертного оцінювання отримано перелік рекомендованих альтернатив електронних навчальних ресурсів, що відповідають заданим критеріям. Наведено загальну структуру рекомендаційної системи вибору електронних навчальних ресурсів

Recommending Learning Objects with Arguments and Explanations

[EN] The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System (C-ERS), which helps students in the process of finding the more appropriated learning resources considering their learning objectives and profile. The recommendation process is based on an argumentation-based approach that selects the learning objects that allow a greater number of arguments to be generated to justify their suitability. Our system includes a simple and intuitive communication interface with the user that provides an explanation to any recommendation. This allows the user to interact with the system and accept or reject the recommendations, providing reasons for such behavior. In this way, the user is able to inspect the system's operation and understand the recommendations, while the system is able to elicit the actual preferences of the user. The system has been tested online with a real group of undergraduate students in the Universidad Nacional de Colombia, showing promising results.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government, and by the Generalitat Valenciana (PROMETEO/2018/002) project.Heras, S.; Palanca Cámara, J.; Rodriguez, P.; Duque-Méndez, N.; Julian Inglada, VJ. (2020). Recommending Learning Objects with Arguments and Explanations. Applied Sciences. 10(10):1-18. https://doi.org/10.3390/app10103341S1181010Zapalska, A., & Brozik, D. (2006). Learning styles and online education. Campus-Wide Information Systems, 23(5), 325-335. doi:10.1108/10650740610714080Rodríguez, P., Heras, S., Palanca, J., Poveda, J. M., Duque, N., & Julián, V. (2017). An educational recommender system based on argumentation theory. AI Communications, 30(1), 19-36. doi:10.3233/aic-170724Chen, L., & Pu, P. (2011). Critiquing-based recommenders: survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1-2), 125-150. doi:10.1007/s11257-011-9108-6He, C., Parra, D., & Verbert, K. (2016). Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications, 56, 9-27. doi:10.1016/j.eswa.2016.02.013Vig, J., Sen, S., & Riedl, J. (2009). Tagsplanations. Proceedings of the 14th international conference on Intelligent user interfaces. doi:10.1145/1502650.1502661Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2009). MoviExplain. Proceedings of the third ACM conference on Recommender systems - RecSys ’09. doi:10.1145/1639714.1639777Fogg, B. J. (2002). Persuasive technology. Ubiquity, 2002(December), 2. doi:10.1145/764008.763957Benbasat, I., & Wang, W. (2005). Trust In and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems, 6(3), 72-101. doi:10.17705/1jais.00065Sikka, R., Dhankhar, A., & Rana, C. (2012). A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications, 47(9), 27-30. doi:10.5120/7218-0024Salehi, M., Pourzaferani, M., & Razavi, S. A. (2013). Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egyptian Informatics Journal, 14(1), 67-78. doi:10.1016/j.eij.2012.12.001Dwivedi, P., & Bharadwaj, K. K. (2013). e-Learning recommender system for a group of learners based on the unified learner profile approach. Expert Systems, 32(2), 264-276. doi:10.1111/exsy.12061Tarus, J. K., Niu, Z., & Mustafa, G. (2017). Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50(1), 21-48. doi:10.1007/s10462-017-9539-5BRIGUEZ, C. E., CAPOBIANCO, M., & MAGUITMAN, A. G. (2013). A THEORETICAL FRAMEWORK FOR TRUST-BASED NEWS RECOMMENDER SYSTEMS AND ITS IMPLEMENTATION USING DEFEASIBLE ARGUMENTATION. International Journal on Artificial Intelligence Tools, 22(04), 1350021. doi:10.1142/s0218213013500218Recio-García, J. A., Quijano, L., & Díaz-Agudo, B. (2013). Including social factors in an argumentative model for Group Decision Support Systems. Decision Support Systems, 56, 48-55. doi:10.1016/j.dss.2013.05.007Briguez, C. E., Budán, M. C. D., Deagustini, C. A. D., Maguitman, A. G., Capobianco, M., & Simari, G. R. (2014). Argument-based mixed recommenders and their application to movie suggestion. Expert Systems with Applications, 41(14), 6467-6482. doi:10.1016/j.eswa.2014.03.046Klašnja-Milićević, A., Ivanović, M., & Nanopoulos, A. (2015). Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44(4), 571-604. doi:10.1007/s10462-015-9440-zThe VARK Questionnaire-Spanish Versionhttps://vark-learn.com/wp-content/uploads/2014/08/The-VARK-Questionnaire-Spanish.pdfGARCÍA, A. J., & SIMARI, G. R. (2004). Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming, 4(1+2), 95-138. doi:10.1017/s1471068403001674Gelfond, M., & Lifschitz, V. (1991). Classical negation in logic programs and disjunctive databases. New Generation Computing, 9(3-4), 365-385. doi:10.1007/bf03037169Snow, R. E. (1991). Aptitude-treatment interaction as a framework for research on individual differences in psychotherapy. Journal of Consulting and Clinical Psychology, 59(2), 205-216. doi:10.1037/0022-006x.59.2.20

Recommending Learning Videos for MOOCs and Flipped Classrooms

[EN] New teaching approaches are emerging in higher education, such as flipped classrooms. In addition, academic institutions are offering new types of training like Massive Online Open Courses. Both of these new ways of education require high-quality learning objects for their success, with learning videos being the most common to provide theoretical concepts. This paper describes a hybrid learning recommender system based on content-based techniques, which is able to recommend useful videos to learners and teachers from a learning video repository. This hybrid technique has been successfully applied to a real scenario such as the central video repository of the Universitat Politècnica de València.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and TIN2017-89156-R projects of the Spanish government, and PROMETEO/2018/002 project of Generalitat Valenciana. J. Jordán and V. Botti are funded by UPV PAID-06-18 project. J. Jordán is also funded by grant APOSTD/2018/010 of Generalitat Valenciana - Fondo Social Europeo.Jordán, J.; Valero Cubas, S.; Turró, C.; Botti Navarro, VJ. (2020). Recommending Learning Videos for MOOCs and Flipped Classrooms. Springer. 146-157. https://doi.org/10.1007/978-3-030-49778-1_12S146157Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowl.-Based Syst. 22(4), 261–265 (2009)Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)Chen, W., Niu, Z., Zhao, X., Li, Y.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2012). https://doi.org/10.1007/s11280-012-0187-zvan Dijck, J., Poell, T.: Higher education in a networked world: European responses to U.S. MOOCs. Int. J. Commun.: IJoC 9, 2674–2692 (2015)Dwivedi, P., Bharadwaj, K.K.: e-learning recommender system for a group of learners based on the unified learner profile approach. Expert Syst. 32(2), 264–276 (2015)Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)Institute and Committee of Electrical and Electronics Engineers: Learning Technology Standards: IEEE Standard for Learning Object Metadata. IEEE Standard 1484.12.1 (2002)Klašnja-Milićević, A., Ivanović, M., Nanopoulos, A.: Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif. Intell. Rev. 44(4), 571–604 (2015). https://doi.org/10.1007/s10462-015-9440-zMaassen, P., Nerland, M., Yates, L. (eds.): Reconfiguring Knowledge in Higher Education. Higher Education Dynamics, vol. 50. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-72832-2MLLP research group, Universitat Politècnica de València: Tlp: The translectures-upv platform. http://www.mllp.upv.es/tlpO’Flaherty, J., Phillips, C.: The use of flipped classrooms in higher education: a scoping review. Internet High. Educ. 25, 85–95 (2015)Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th international conference on World Wide Web, pp. 521–530 (2007)Rodríguez, P., Heras, S., Palanca, J., Duque, N., Julián, V.: Argumentation-based hybrid recommender system for recommending learning objects. In: Rovatsos, M., Vouros, G., Julian, V. (eds.) EUMAS/AT -2015. LNCS (LNAI), vol. 9571, pp. 234–248. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33509-4_19Roehl, A., Reddy, S.L., Shannon, G.J.: The flipped classroom: an opportunity to engage millennial students through active learning strategies. J. Fam. Consum. Sci. 105, 44–49 (2013)Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)Stoica, A.S., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C.: A semi-supervised method to classify educational videos. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 218–228. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_19Tarus, J.K., Niu, Z., Yousif, A.: A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Gener. Comput. Syst. 72, 37–48 (2017)Tucker, B.: The flipped classroom. Online instruction at home frees class time for learning. Educ. Next Winter 2012, 82–83 (2012)Turcu, G., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C.: Towards a custom designed mechanism for indexing and retrieving video transcripts. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 299–309. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_26Turró, C., Morales, J.C., Busquets-Mataix, J.: A study on assessment results in a large scale flipped teaching experience. In: 4th International Conference on Higher Education Advances (HEAD 2018), pp. 1039–1048 (2018)Turró, C., Despujol, I., Busquets, J.: Networked teaching, the story of a success on creating e-learning content at Universitat Politècnica de València. EUNIS J. High. Educ. (2014)Zajda, J., Rust, V. (eds.): Globalisation and Higher Education Reforms. GCEPR, vol. 15. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28191-

An educational recommender system based on argumentation theory

You are free to use the manuscript version of your article for internal, educational or other purposes of your own institution, company or funding agency[EN] Recommender Systems aim to provide users with search results close to their needs, making predictions of their preferences. In virtual learning environments, Educational Recommender Systems deliver learning objects according to the student's characteristics, preferences and learning needs. A learning object is an educational content unit, which once found and retrieved may assist students in their learning process. In previous work, authors have designed and evaluated several recommendation techniques for delivering the most appropriate learning object for each specific student. Also, they have combined these techniques by using hybridization methods, improving the performance of isolated techniques. However, traditional hybridization methods fail when the learning objects delivered by each recommendation technique are very different from those selected by the other techniques (there is no agreement about the best learning object to recommend). In this paper, we present a new recommendation method based on argumentation theory that is able to combine content-based, collaborative and knowledge-based recommendation techniques, or to act as a new recommendation technique. This method provides the students with those objects for which the system is able to generate more arguments to justify their suitability. It has been implemented and tested in the Federation of Learning Objects Repositories of Colombia, getting promising results.This work was partially developed with the aid of the doctoral grant offered to Paula A. Rodriguez by 'Programa Nacional de Formacion de Investigadores - COLCIENCIAS', Colombia and partially funded by the COLCIENCIAS project 1119-569-34172 from the Universidad Nacional de Colombia. It was also supported by the by the projects TIN2015-65515-C4-1-R and TIN2014-55206-R of the Spanish government and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politecnica de Valencia.Rodríguez, P.; Heras, S.; Palanca Cámara, J.; Poveda, JM.; Duque, N.; Julian Inglada, VJ. (2017). An educational recommender system based on argumentation theory. AI Communications. 30(1):19-36. https://doi.org/10.3233/AIC-170724S1936301Briguez, C. E., Budán, M. C. D., Deagustini, C. A. D., Maguitman, A. G., Capobianco, M., & Simari, G. R. (2014). Argument-based mixed recommenders and their application to movie suggestion. Expert Systems with Applications, 41(14), 6467-6482. doi:10.1016/j.eswa.2014.03.046BRIGUEZ, C. E., CAPOBIANCO, M., & MAGUITMAN, A. G. (2013). A THEORETICAL FRAMEWORK FOR TRUST-BASED NEWS RECOMMENDER SYSTEMS AND ITS IMPLEMENTATION USING DEFEASIBLE ARGUMENTATION. International Journal on Artificial Intelligence Tools, 22(04), 1350021. doi:10.1142/s0218213013500218R. Burke, Hybrid recommender systems: Survey and experiments, User Modelingand User-Adapted Interaction (2002).Chesñevar, C., Maguitman, A. G., & González, M. P. (2009). Empowering Recommendation Technologies Through Argumentation. Argumentation in Artificial Intelligence, 403-422. doi:10.1007/978-0-387-98197-0_20Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of Recommender Systems to Support Learning. Recommender Systems Handbook, 421-451. doi:10.1007/978-1-4899-7637-6_12N.D. Duque, D.A. Ovalle and J. Moreno, Objetos de aprendizaje, repositorios y federaciones... conocimiento para todos. Universidad Nacional de Colombia, 2015.Dwivedi, P., & Bharadwaj, K. K. (2013). e-Learning recommender system for a group of learners based on the unified learner profile approach. Expert Systems, 32(2), 264-276. doi:10.1111/exsy.12061GARCÍA, A. J., & SIMARI, G. R. (2004). Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming, 4(1+2), 95-138. doi:10.1017/s1471068403001674Gunawardana, A., & Shani, G. (2015). Evaluating Recommender Systems. Recommender Systems Handbook, 265-308. doi:10.1007/978-1-4899-7637-6_8Heras, S., Botti, V., & Julián, V. (2012). Argument-based agreements in agent societies. Neurocomputing, 75(1), 156-162. doi:10.1016/j.neucom.2011.02.022Heras, S., Rebollo, M., & Julián, V. (s. f.). A Dialogue Game Protocol for Recommendation in Social Networks. Hybrid Artificial Intelligence Systems, 515-522. doi:10.1007/978-3-540-87656-4_64P.A. Kirschner, S.J. Buckingham-Shum and C.S. Carr, Visualizing Argumentation: Software Tools for Collaborative and Educational Sense-Making, Springer Science & Business Media, 2012.Klašnja-Milićević, A., Ivanović, M., & Nanopoulos, A. (2015). Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44(4), 571-604. doi:10.1007/s10462-015-9440-zLearning Technology Standards Committee, IEEE Standard for Learning Object Metadata, Institute of Electrical and Electronics Engineers, New York, 2002.Leite, W. L., Svinicki, M., & Shi, Y. (2009). Attempted Validation of the Scores of the VARK: Learning Styles Inventory With Multitrait–Multimethod Confirmatory Factor Analysis Models. Educational and Psychological Measurement, 70(2), 323-339. doi:10.1177/0013164409344507Li, H., Oren, N., & Norman, T. J. (2012). Probabilistic Argumentation Frameworks. Lecture Notes in Computer Science, 1-16. doi:10.1007/978-3-642-29184-5_1CACM Staff. (2009). Recommendation algorithms, online privacy, and more. Communications of the ACM, 52(5), 10-11. doi:10.1145/1506409.1506434Ossowski, S., Sierra, C., & Botti, V. (2012). Agreement Technologies: A Computing Perspective. Agreement Technologies, 3-16. doi:10.1007/978-94-007-5583-3_1Palanca, J., Heras, S., Jorge, J., & Julian, V. (2015). Towards persuasive social recommendation. ACM SIGAPP Applied Computing Review, 15(2), 41-49. doi:10.1145/2815169.2815173Recio-García, J. A., Quijano, L., & Díaz-Agudo, B. (2013). Including social factors in an argumentative model for Group Decision Support Systems. Decision Support Systems, 56, 48-55. doi:10.1016/j.dss.2013.05.007Rodríguez, P., Duque, N., & Ovalle, D. A. (2015). Multi-agent System for Knowledge-Based Recommendation of Learning Objects Using Metadata Clustering. Communications in Computer and Information Science, 356-364. doi:10.1007/978-3-319-19033-4_31Rodríguez, P. A., Ovalle, D. A., & Duque, N. D. (2015). A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories. Learning and Collaboration Technologies, 291-300. doi:10.1007/978-3-319-20609-7_28M. Salehi, M. Pourzaferani and S.A. Razavi, Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model, Egyptian Informatics Journal (2013).Sikka, R., Dhankhar, A., & Rana, C. (2012). A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications, 47(9), 27-30. doi:10.5120/7218-0024Sinha, R., & Swearingen, K. (2002). The role of transparency in recommender systems. CHI ’02 extended abstracts on Human factors in computing systems - CHI ’02. doi:10.1145/506443.506619Van de Sompel, H., Chute, R., & Hochstenbach, P. (2008). The aDORe federation architecture: digital repositories at scale. International Journal on Digital Libraries, 9(2), 83-100. doi:10.1007/s00799-008-0048-7Vekariya, V., & Kulkarni, G. R. (2012). Notice of Violation of IEEE Publication Principles - Hybrid recommender systems: Survey and experiments. 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP). doi:10.1109/dictap.2012.621540

Knowledge aggregation in people recommender systems : matching skills to tasks

People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives.Postprint (published version

How Simulation can Illuminate Pedagogical and System Design Issues in Dynamic Open Ended Learning Environments

A Dynamic Open-Ended Learning Environment (DOELE) is a collection of learners and learning objects (LOs) that could be constantly changing. In DOELEs, learners need the support of Advanced Learning Technology (ALT), but most ALT is not designed to run in such environments. An architecture for designing advanced learning technology that is compatible with DOELEs is the ecological approach (EA). This thesis looks at how to test and develop ALT based on the EA, and argues that this process would benefit from the use of simulation. The essential components of an EA-based simulation are: simulated learners, simulated LOs, and their simulated interactions. In this thesis the value of simulation is demonstrated with two experiments. The first experiment focuses on the pedagogical issue of peer impact, how learning is impacted by the performance of peers. By systematically varying the number and type of learners and LOs in a DOELE, the simulation uncovers behaviours that would otherwise go unseen. The second experiment shows how to validate and tune a new instructional planner built on the EA, the Collaborative Filtering based on Learning Sequences planner (CFLS). When the CFLS planner is configured appropriately, simulated learners achieve higher performance measurements that those learners using the baseline planners. Simulation results lead to predictions that ultimately need to be proven in the real world, but even without real world validation such predictions can be useful to researchers to inform the ALT system design process. This thesis work shows that it is not necessary to model all the details of the real world to come to a better understanding of a pedagogical issue such as peer impact. And, simulation allowed for the design of the first known instructional planner to be based on usage data, the CFLS planner. The use of simulation for the design of EA-based systems opens new possibilities for instructional planning without knowledge engineering. Such systems can find niche learning paths that may have never been thought of by a human designer. By exploring pedagogical and ALT system design issues for DOELEs, this thesis shows that simulation is a valuable addition to the toolkit for ALT researchers

Knowledge aggregation in people recommender systems : matching skills to tasks

People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives

Sistema de recomendação de objetos de aprendizagem digitais para e-learning: um estudo de caso em curso superior à distância da UFSC

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Ciências da Educação, Programa Pós-Graduação em Ciência da Informação, Florianópolis, 2017.A adoção de plataformas de e-learning (aprendizagem eletrônica) para a educação a distância (EaD) de universidades ao redor do mundo, tem sido uma das opções para estudantes que buscam flexibilidade e formação por meio da autoaprendizagem. Nesse aspecto, abre-se espaço para estudos de meios de trazer benefícios aos estudantes, dentre eles, promover acesso às informações que possam propiciar melhorias no fluxo contínuo no processo de aprendizagem. Um Sistema de Recomendação de Objetos de Aprendizagem Digitais possibilita contribuir com esse processo, além de atenuar as dificuldades face às complexidades nos processos de recuperação de informação relevante, devido à sobrecarga informacional nos repositórios dos cursos assim como na web. Os estudos acerca desses sistemas tem como premissa a sugestão de objetos, relacionados ao nível em que o estudante se encontra, como forma de apoiá-lo e contribuir para sua evolução. Nesta perspectiva, a presente pesquisa tem como objetivo geral ?Analisar como um Sistema de Recomendação de Objetos de Aprendizagem Digitais pode contribuir com estudantes de curso superior em modalidade de e-learning.?. Como estudo de caso, adota-se o curso de Licenciatura em Letras ? Espanhol (EaD/UFSC). No referencial teórico aborda-se a Recuperação de Informação, os Sistemas de Recomendação no contexto do e-learning, os Objetos de Aprendizagem Digitais e as tecnologias de Agentes Inteligentes e ainda, se estabelece articulação com a Ciência da Informação. A pesquisa caracteriza-se, quanto aos objetivos, como pesquisa bibliográfica de caráter exploratório que emprega a técnica de análise de conteúdo; quanto à forma de abordagem, como pesquisa qualitativa e quanto à revisão teórica, uma revisão sistemática. Como resultados foram elaborados o Modelo de Banco de Dados para armazenamento dos dados, bem como o Fluxo de Informação do Sistema de Recomendação para e-learning, para representar os processos de acesso às informações organizadas e armazenadas no banco. Um dos pontos relevantes, caracterizado neste trabalho, é o fato de que um Sistema de Recomendação permite ampliar o poder de recomendação de Objetos de Aprendizagem Digitais do professor. Sob a perspectiva de planejamento e organização da informação para uso, conclui-se ser viável a continuidade nas pesquisas para que os Sistemas de Recomendação possam ser adotados não somente no ambiente de estudo de caso desta pesquisa, assim como em quaisquer outros ambientes de aprendizagem com adoção de novas formas (modelos, técnicas, alvos, entre outros) de implementação.Abstract : The adoption of e-learning platforms for distance education by universities around the world has been one of the options for students seeking flexibility and training through self-learning. In this context, research is required on ways to benefit the students, promoting access to information that can improve the continuous flow of the learning process. A Recommender System of Digital Learning Objects can contribute to this process and, additionally, alleviate the difficulties faced in the complex processes of retrieving relevant information, due to the informational overload in the course?s repositories as well as on the web. Studies about these systems are based on the suggestion of objects, related to the level at which the student is at, as a way to support and contribute to their evolution. In this perspective, the present research has as general objective: "To analyze how a Recommender System of Digital Learning Objects can support the students of e-learning graduation course". As a case study, it has been chosen the e-learning degree in Letters - Spanish (EaD / UFSC). In the theoretical reference it is addressed the Information Retrieval, the Recommender Systems in the context of e-learning, the Digital Learning Objects and the Intelligent Agents technologies, and it is also established the relation with Information Science. The research is characterized as regards to the objectives as: an exploratory bibliographic research that employs the technique of content analysis; qualitative research; and a systematic review. As result, the Database Model was developed, as well as the Information Flow of the Recommender System for e-learning, to represent the processes for accessing the information organized and stored in the database. One of the key points characterized in this work is the fact that a Recommender System improves the quality of the teacher?s recommendations of Digital Learning Objects. From the perspective of planning and organizing information, it was concluded that follow up research is viable so that Recommender Systems can be adopted not only in the case study of this research, but in any other learning environment adopting new methods (models, techniques, targets, among others) of implementation