1,101 research outputs found

    Content wizard: Concept-based recommender system for instructors of programming courses

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    Authoring an adaptive educational system is a complex process that involves allocating a large range of educational content within a fixed sequence of units. In this paper, we describe Content Wizard, a concept-based recommender system for recommending learning materials that meet the instructor's pedagogical goals during the creation of an online programming course. Here, the instructors are asked to provide a set of code examples that jointly re.ect the learning goals that are associated with each course unit. The Wizard is built on top of our course-authoring tool, and it helps to decrease the time instructors spend on the task and to maintain the coherence of the sequential structure of the course. It also provides instructors with additional information to identify content that might be not appropriate for the unit they are creating. We conducted an o.- line study with data collected from an introductory Java course previously taught at the University of Pittsburgh in order to evaluate both the practicality and effectiveness of the system. We found that the proposed recommendation's performance is relatively close to the teacher's expectation in creating a computer-based adaptive course

    Supporting the development of mobile adaptive learning environments: A case study

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    Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. E. Martín and R. M. Carro, "Supporting the development of mobile adaptive learning environments: A case study" IEEE Transactions on learning technologies, vol. 2, no. 1, pp. 23-36, january-march 2009In this paper, we describe a system to support the generation of adaptive mobile learning environments. In these environments, students and teachers can accomplish different types of individual and collaborative activities in different contexts. Activities are dynamically recommended to users depending on different criteria (user features, context, etc.), and workspaces to support the corresponding activity accomplishment are dynamically generated. In this paper, we present the main characteristics of the mechanism that suggests the most suitable activities at each situation, the system in which this mechanism has been implemented, the authoring tool to facilitate the specification of context-based adaptive m-learning environments, and two environments generated following this approach will be presented. The outcomes of two case studies carried out with students of the first and second courses of “Computer Engineering” at the “Universidad Auto´noma de Madrid” are also presented.This work has been supported by the Spanish Ministry of Science and Education, project number TIN2007-64718

    Domain Modeling for Personalized Guidance

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    This chapter attempts to untangle the relationships between personalized guidance and domain modeling, as well as to explain how domain modeling could be used to provide personalized guidance. The problem of personalized guidance has a long history in the area of adaptive educational systems (AES). In fact, the very first recognized AES SCHOLAR (Carbonell, 1970) focused on guiding students to the most relevant facts and questions about the geography of South America. The SCHOLAR functionality was based on a domain model in the form of a semantic network and an overlay student model. Since that time, a considerable share of research in the field of AES has focused on different kinds of personalized guidance, and the majority of this work relied heavily on domain modeling—which makes these two research directions heavily interconnected

    Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

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    The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

    Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

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    The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

    Accessible lifelong learning at higher education:outcomes and lessons Learned at two different PilotSites in the EU4ALL Project

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    [EN] The EU4ALL project (IST-FP6-034778) has developed a general framework to address the needs of accessible lifelong learning at Higher Education level consisting of several standards-based interoperable components integrated into an open web service architecture aimed at supporting adapted interaction to guarantee students' accessibility needs. Its flexibility has supported the project implementation at several sites with different settings and various learning management systems. Large-scale evaluations involving hundreds of users, considering diverse disability types, and key staff roles have allowed obtaining valuable lessons with respect to "how to adopt or enhance eLearning accessibility" at university. The project was evaluated at four higher education institutions, two of the largest in Europe and two mediumsized. In this paper, we focus on describing the implementation and main conclusions at the largest project evaluation site (UNED), which was involved in the project from the beginning, and thus, in the design process, and a medium-sized university that adopted the EU4ALL approach (UPV). This implies dealing with two well-known open source learning environments (i.e. dotLRN and Sakai), and considering a wide variety of stakeholders and requirements. Thus the results of this evaluation serve to illustrate the coverage of both the approach and developments.The authors would like to thank the European Commission for the financial support of the EU4ALL project (IST-2006-034478). The work at aDeNu is also supported by the Spanish Ministry of Science and Innovation (TIN2008-06862-C04-01/TSI “A2UN@”). Authors would also like to thank all the EU4ALL partners for their collaboration.Boticario, JG.; Rodriguez-Ascaso, A.; Santos, OC.; Raffenne, E.; Montandon, L.; Roldán Martínez, D.; Buendía García, F. (2012). Accessible lifelong learning at higher education:outcomes and lessons Learned at two different PilotSites in the EU4ALL Project. Journal of Universal Computer Science. 18(1):62-85. http://hdl.handle.net/10251/37117628518

    Prototyping Adaptive Online Learning Courses

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    This article describes the process of prototyping adaptive online learning using the authoring tool for developers, which is based on ontologies. The article also gives a brief overview of contemporary situation and describes modern trends of evolution e-learning courses and present standards in this area. It also describes architecture of system VITA II

    Ontology-based Recommendation System for Personalized Education of Dyslexics to Read

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    Dyslexic pupils have learning difficulties in reading, writing, maths. They require individualised education. The paper presents an approach to achievement of personalised teaching the basics of reading that applies dyslexic learner’ profile ontology and learning goals ontology. A general model of reading that is rationale for determination of the latter is proposed. Ontology of dyslexia disabilities is suggested for manual annotation of learning resources
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