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. 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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). 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On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user pro le by following the principles of the I-Change model and maintaining the bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.European Union's Horizon 2020 Research and Innovation Programme under Grant 68112

Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

Drachsler, H., Hummel, H. G. K., & Koper, R. (2009). Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning. Journal of Digital Information, 10(2), 4-24.The following article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning to support lifelong learners. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system from one context and transfer it to another context or domain. The article describes a number of distinct differences for personalized recommendation to consumers in contrast to recommendations to learners. Similarities and differences are translated into specific demands for learning and specific requirements for personal recommendation systems. It further suggests an evaluation approach for recommender systems in technology-enhanced learning.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Advances in Writing Analytics: Mapping the state of the field

Writing analytics as a field is growing in terms of the tools and technologies developed to support student writing, methods to collect and analyze writing data, and the embedding of tools in pedagogical contexts to make them relevant for learning. This workshop will facilitate discussion on recent writing analytics research by researchers, writing tool developers, theorists and practitioners to map the current state of the field, identify issues and develop future directions for advances in writing analytics

Definition: A Three-Dimensional Analysis with Bearing on Key Concepts

This essay presents a three-dimensional analysis of definition (form, stance, and content) with application to making and evaluating definitions; teaching how to define; avoiding equivocation with argument and bias ; and, using the concept-conception distinction, avoiding being deterred by the many definitions of critical thinking , and seeing the usefulness of objectivity in everyday arguments in spite of existing conflict and confusion about aspects of objectivity