The information overload in learning and teaching scenarios is a main hindering factor for efficient and effective learning. New methods are needed to help teachers and students in dealing with the ...vast amount of available information and learning material. Our approach aims to utilize contextualized attention metadata to capture behavioural information of users in learning contexts that can be used to deal with the information overload in user centric ways. We introduce a schema and framework for capturing and managing such contextualized attention metadata in this paper. Schema and framework are designed to enable collecting and merging observations about the attention users give to content and their contexts. The contextualized attention metadata schema enables the correlation of the observations, thus reflects the relationships that exists between the user, her context and the content she works with. We illustrate with a simple demo application how contextualized attention metadata can be collected from several tools, the merging of the data streams into a repository and finally the correlation of the data.
In this paper, we introduce a new way of detecting semantic similarities between learning objects by analysing their usage in web portals. Our approach relies on the usage-based relations between the ...objects themselves rather then on the content of the learning objects or on the relations between users and learning objects. We then take this new similarity measure to enhance existing recommendation approaches for the use in technology enhanced learning.
Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming ...variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.
Self-regulated learning (SRL) environments provide students with activities to improve their learning (e.g., by solving exercises), but they might also provide optional activities (e.g., changing an ...avatar image or setting goals) where students can decide whether they would like to use or do them and how. Few works have dealt with the use of optional activities in SRL environments. This paper thus analyzes the use of optional activities in two case studies with a SRL approach. We found that the level of use of optional activites was low with only 23.1 percent of students making use of some functionality, while the level of use of learning activities was higher. Optional activities which are not related to learning are used more. We also explored the behavior of students using some of the optional activities in the courses such as setting goals and voting comments, finding that students finished the goals they set in more than 50 percent of the time and that they voted their peers' comments in a positive way. We also found that gender and the type of course can influence which optional activities are used. Moreover, the relations of the use of optional activities with proficient exercises and learning gains is low when taking out third variables, but we believe that optional activities might motivate students and produce better learning in an indirect way.
This paper raises the issue of missing data sets for recommender systems in Technology Enhanced Learning that can be used as benchmarks to compare different recommendation approaches. It discusses ...how suitable data sets could be created according to some initial suggestions, and investigates a number of steps that may be followed in order to develop reference data sets that will be adopted and reused within a scientific community. In addition, policies are discussed that are needed to enhance sharing of data sets by taking into account legal protection rights. Finally, an initial elaboration of a representation and exchange format for sharable TEL data sets is carried out. The paper concludes with future research needs.
The growing amount of available information on the internet makes the process of filtering appropriate information an increasing challenge. Because currently existing approaches provide insufficient ...results in many cases, we propose a new way of relating objects based on their usage. We assume that objects which are significantly often used in the same session are semantically related. Thus, we build a usage-based relatedness graph, apply a graph-based clustering algorithm and evaluate the results with respect to semantic similarity measures. Our approach takes the learning domain into special consideration, its evaluation is performed within the Learning Object Repository MACE.
RDF-based P2P networks have a number of advantages compared to simpler P2P networks such as Napster, Gnutella or to approaches based on distributed indices on binary keys such as CAN and CHORD. ...RDF-based P2P networks allow complex and extendable descriptions of resources instead of fixed and limited ones, and they provide complex query facilities against these metadata instead of simple keyword-based searches.
In this paper, we will discuss RDF-based P2P networks like Edutella as a specific example of a new type of P2P networks-schema-based P2P networks—and describe the use of super-peer based topologies for these networks. Super-peer based networks can provide better scalability than broadcast based networks, and provide support for inhomogeneous schema-based networks, with different metadata schemas and ontologies (crucial for the Semantic Web). Based on (dynamic) metadata routing indices, stated in RDF, the super-peer network supports sophisticated routing and distribution strategies, as well as preparing the ground for advanced mediation and clustering functionalities.
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