10 research outputs found

    Using online presence data for recommending human resources in the OP4L project

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    International audienceIn order to help and support learning practices, the development of web-based Personal Learning Environments (PLE) is widely adopted. A PLE is a set of services customized by the student. Among these services, resource (either digital or human) recommendation is a crucial one. The paper briefly reviews existing approaches for recommending resources in PLE. Then it describes a novel approach implemented in the OP4L prototype. OP4L is combining Social Web presence data and semantic web technologies, based on an intensive use of ontological models to represent the learning context. Then the paper reports about qualitative studies that were conducted with students on the currently available version of the OP4L prototype. The aim of the study was to get students' feedbacks about new online presence services offered in a LMS

    Using online presence data for recommending human resources in the OP4L project

    Get PDF
    International audienceIn order to help and support learning practices, the development of web-based Personal Learning Environments (PLE) is widely adopted. A PLE is a set of services customized by the student. Among these services, resource (either digital or human) recommendation is a crucial one. The paper briefly reviews existing approaches for recommending resources in PLE. Then it describes a novel approach implemented in the OP4L prototype. OP4L is combining Social Web presence data and semantic web technologies, based on an intensive use of ontological models to represent the learning context. Then the paper reports about qualitative studies that were conducted with students on the currently available version of the OP4L prototype. The aim of the study was to get students' feedbacks about new online presence services offered in a LMS

    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 Investigaci贸n y Formaci贸n en Inform谩tica 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 Investigaci贸n y Formaci贸n en Inform谩tica Avanzad

    Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

    Get PDF
    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 Investigaci贸n y Formaci贸n en Inform谩tica Avanzad

    Personalized Recommender Systems for Resource-based Learning - Hybrid Graph-based Recommender Systems for Folksonomies

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    As the Web increasingly pervades our everyday lives, we are faced with an overload of information. We often learn on-the-job without a teacher and without didactically prepared learning resources. We not only learn on our own but also collaboratively on social platforms where we discuss issues, exchange information and share knowledge with others. We actively learn with resources we find on the Web such as videos, blogs, forums or wikis. This form of self-regulated learning is called resource-based learning. An ongoing challenge in technology enhanced learning (TEL) and in particular in resource-based learning, is supporting learners in finding learning resources relevant to their current needs and learning goals. In social tagging systems, users collaboratively attach keywords called tags to resources thereby forming a network-like structure called a folksonomy. Additional semantic information gained for example from activity hierarchies or semantic tags, form an extended folksonomy and provide valuable information about the context of the resources the learner has tagged, the related activities the resources could be relevant for, and the learning task the learner is currently working on. This additional semantic information could be exploited by recommender systems to generate personalized recommendations of learning resources. Thus, the first research goal of this thesis is to develop and evaluate personalized recommender algorithms for a resource-based learning scenario. To this end, the resource-based learning application scenario is analysed, taking an existing learning platform as a concrete example, in order to determine which additional semantic information could be exploited for the recommendation of learning resources. Several new hybrid graph-based recommender approaches are implemented and evaluated. Additional semantic information gained from activities, activity hierarchies, semantic tag types, the semantic relatedness between tags and the context-specific information found in a folksonomy are thereby exploited. The proposed recommender algorithms are evaluated in offline experiments on different datasets representing diverse evaluation scenarios. The evaluation results show that incorporating additional semantic information is advantageous for providing relevant recommendations. The second goal of this thesis is to investigate alternative evaluation approaches for recommender algorithms for resource-based learning. Offline experiments are fast to conduct and easy to repeat, however they face the so called incompleteness problem as datasets are limited to the historical interactions of the users. Thus newly recommended resources, in which the user had not shown an interest in the past, cannot be evaluated. The recommendation of novel and diverse learning resources is however a requirement for TEL and needs to be evaluated. User studies complement offline experiments as the users themselves judge the relevance or novelty of the recommendations. But user studies are expensive to conduct and it is often difficult to recruit a large number of participants. Therefore a gap exists between the fast, easy to repeat offline experiments and the more expensive user studies. Crowdsourcing is an alternative as it offers the advantages of offline experiments, whilst still retaining the advantages of a user-centric evaluation. In this thesis, a crowdsourcing evaluation approach for recommender algorithms for TEL is proposed and a repeated evaluation of one of the proposed recommender algorithms is conducted as a proof-of-concept. The results of both runs of the experiment show that crowdsourcing can be used as an alternative approach to evaluate graph-based recommender algorithms for TEL

    Entornos personales de Aprendizaje M贸vil (mPLE) en la Educaci贸n Superior

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    [ES]A nivel universitario, los entornos personales de aprendizaje surgen como una alternativa para solventar las deficiencias de las plataformas de aprendizaje institucionales, al ser espacios educativos centrados en el estudiante y potenciados por las tecnolog铆as de la informaci贸n y comunicaci贸n, y que facilitan el aprendizaje informal. Sin embargo la mayor铆a de investigaciones publicadas se basan 煤nicamente en el uso de ordenadores sin tomar en cuenta los contextos m贸viles ampliamente usados a nivel mundial. En este sentido, el objetivo del presente trabajo de investigaci贸n es dise帽ar, implementar y evaluar la integraci贸n de Entornos Personales de Aprendizaje M贸viles (mPLE) en los procesos de ense帽anza-aprendizaje en la carrera de Ingenier铆a en Sistemas y Computaci贸n de la Universidad Nacional de Chimborazo (Ecuador), con el fin de mejorar el nivel y la experiencia de aprendizaje en los estudiantes. Desde el punto de vista metodol贸gico corresponde a un modelo de investigaci贸n mixto, donde se integra el enfoque cuantitativo y cualitativo para el tratamiento de la informaci贸n. As铆, en la parte cuantitativa se utilizan cuestionarios previamente validados, que se han aplicado a estudiantes de manera presencial y online, cuyos resultados han permitido comprobar las hip贸tesis de investigaci贸n planteadas. En la parte cualitativa se trabaj贸 por medio de entrevistas realizadas en grupos focales para conocer las expectativas de los estudiantes acerca de la incorporaci贸n de los mPLE en su aprendizaje, as铆 como las ventajas y desventajas de estas innovaciones. Los resultados muestran diferencias significativas en cuanto a los niveles del aprendizaje alcanzado entre quienes trabajaron con estos nuevos entornos educativos y quienes no lo hicieron, as铆 como tambi茅n sobre las percepciones de aprendizaje en t茅rminos de autonom铆a, flexibilidad, interacci贸n y movilidad. En conclusi贸n el dise帽o e implementaci贸n de los mPLE en el colectivo universitario estudiado incide positivamente tanto en sus niveles de aprendizaje como en las experiencias de aprendizaje percibidas
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