Titlebook: Recommender Systems for Technology Enhanced Learning; Research Trends and Nikos Manouselis,Hendrik Drachsler,Olga C. Santos Book 2014 Spri

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Collaborative Filtering Recommendation of Educational Content in Social Environments Utilizing Senti While metrics based on access patterns and user behaviour produce interesting results, they do not take into account qualitative information, i.e. the actual opinion of a user that used the resource and whether or not he would propose it for use to other users. This is of particular importance on e

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Towards Automated Evaluation of Learning Resources Inside Repositoriesty given by the members of the repository community. Although this strategy can be considered effective at some extent, the number of resources inside repositories tends to increase more rapidly than the number of evaluations given by this community, thus leaving several resources of the repository

爱社交

A Survey on Linked Data and the Social Web as Facilitators for TEL Recommender Systems as part of TEL recommender systems to filter and recommend learning resources or peer learners according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the quality and quantity of available data, for instance, metad

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The Learning Registry: Applying Social Metadata for Learning Resource Recommendations valuable for curating digital collections, is difficult to keep current or, in some cases, to obtain in the first place. Social metadata, paradata, usage data, and contextualized attention metadata all refer to data about . digital resources that can be harnessed for recommendations. To centralize

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An Approach for an Affective Educational Recommendation Modelir ability to increase the performance of recommender systems in non-educational scenarios. In our work, we combine both research lines and describe the SAERS approach to model affective educational recommendations. This affective recommendation model has been initially validated with the applicatio

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The Case for Preference-Inconsistent Recommendationsnexploited: Learners prefer preference-consistent over preference-inconsistent information, a phenomenon called confirmation bias. This chapter attempts to introduce how recommender systems can be used to stimulate unbiased information selection, elaboration and unbiased evaluation. The principle of

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Further Thoughts on Context-Aware Paper Recommendations for Educationn. As such, we proposed the multidimensional recommendation techniques that consider (educational) context-aware information to inform and guide the system during the recommendation process. The contextual information includes both learner and paper features that can be extracted and learned during

FID

Towards a Social Trust-Aware Recommender for Teachersn order to develop their personal and professional skills. However, with the large number of learning resources produced every day, teachers need to find out what are the most suitable ones for them. In this paper, we introduce recommender systems as a potential solution to this. The setting is the

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ALEF: From Application to Platform for Adaptive Collaborative Learningon, learning tailored to students’ individual preferences, and collaboration. The range of Web 2.0 tools and features is constantly evolving, with focus on users and ways that enable users to socialize, share and work together on (user-generated) content. In this chapter we present ALEF—Adaptive Lea