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Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But, the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user satisfaction should also be performed before deploying a recommender system in a real target environment. Such evaluations are laborious and complicated compared to the traditional, data-centric evaluations, though. In this study, we carried out a user-centric evaluation of state-of-the-art recommender systems as well as a graph-based approach in the ecologically valid setting of an authentic social learning platform. We also conducted a data-centric evaluation on the same data to investigate the added value of user-centric evaluations and how user satisfaction of a recommender system is related to its performance in terms of accuracy metrics. Our findings suggest that user-centric evaluation results are not necessarily in line with data-centric evaluation results. We conclude that the traditional evaluation of recommender systems in terms of prediction accuracy only does not suffice to judge performance of recommender systems on the user side. Moreover, the user-centric evaluation provides valuable insights in how candidate algorithms perform on each of the five quality metrics for recommendations: usefulness, accuracy, novelty, diversity, and serendipity.

Multiuser immersive learning scenarios hold strong potential for lifelong learning as they can support the acquisition of higher order skills in an effective, efficient, and attractive way. Existing virtual worlds, game development platforms, and game engines only partly cater for the proliferation of such learning scenarios as they are often inadequately tuned for learning. First, this study aims to identify architectures that more effectively support the development of multiuser immersive learning scenarios. Second, this study takes up the challenge to define and assemble more flexible architectures that cater for fast and easy development, which will become important in the current period of economic breakdown. Third, this study describes how such architectures should enable research into guidelines for multiuser immersive learning scenario design and development. This study outlines a method for defining and setting up such architectures by using experts and existing literature.

In Chap. 11 we presented placement in the context of Accreditation of Prior Learning and showed that in the scenario we address we do not assume the availability of controlled vocabulary with which the contents of the learner portfolio or the learning material in the learning network is described. Our placement service is based on the assumption that similarity between material produced or studied by the learner on the one hand and the learning material of the learning network on the other, can be used as a proxy to similarity in learning outcomes. The first task of any such placement service is therefore to establish whether these similarities are present for a given learner. The technology with which this is done, latent semantic analysis, is presented here. The emphasis here is on the technical and computational aspects of data preparation and analysis. Open image in new window

In Cooper, part of the student support will be provided by a Question Answering application in the form of a webservice. Question Answering allows a user to use the content of project document as input to find related documents as well as related experts. Latent Semantic Analysis as an underlying technique is briefly discussed followed by a description of our Latent Semantic Analysis engine and the software architecture that was developed. Issues for further development are also mentioned. The final section contains a specific case study of an environment in which an implementation is planned.

Dit artikel gaat in op de educatieve mogelijkheden van dvd-video.Na een korte introductie van het medium dvd-video wordt een drietal praktijkvoorbeelden besproken. Het eerste voorbeeld is het programma ‘Sociale psychologie’, dat zich richt op het opzetten en uitvoeren van sociaal-psychologische experimenten. Het tweede voorbeeld is het dvd-videoprogramma ‘Het selectieproces’, een trainingsprogramma over het proces van personeelswerving en -selectie.

This contribution introduces a placement web-service which has been developed in the context of the TENCompetence Integrated Project. The web-service uses Latent Semantic Analysis (LSA) to calculate similarity between documents in learner portfolios and content in the current learning network of the learner. In the first part of the paper the placement problem in learning networks is introduced. Next we discuss the architecture of the current web-service prototype and describe our evaluation approach. Several limitations and future developments are discussed at the end of the paper.