Collaborative recommendation of e‐learning resources: an experimental investigation

Abstract

Repositories with educational resources can support the formation of online learning communities by providing a platform for collaboration. Users (e.g. teachers, tutors and learners) access repositories, search for interesting resources to access and use, and in many cases, also exchange experiences and opinions. A particular class of online services that take advantage of the collected knowledge and experience of users are collaborative filtering ones. The successful operation of such services in the context of real-life applications requires careful testing and parameterization before their actual deployment. In this paper, the case of developing a learning resources' collaborative filtering service for an online community of teachers in Europe was examined. More specifically, a data set of evaluations of learning resources was collected from the teachers that use the European Schoolnet's learning resource portal. These evaluations were then used to support the experimental investigation of design choices for an online collaborative filtering service for the portal's learning resources. A candidate multi-attribute utility collaborative filtering algorithm was appropriately parameterized and tested for this purpose. Results indicated that the development of such systems should be taking place considering the particularities of the actual communities that are to be served.

A Survey on Linked Data and the Social Web as Facilitators for TEL Recommender Systems

“…A more thorough overview of educational Linked Data is offered by the Linked Education 29 platform and in [4]. In the TEL field many research projects are working with rather small internal datasets which cannot be shared with other research institutes [48] [49]. Therefore, the EATEL Special Interest Group dataTEL was founded [43] with a focus on the analysis of issues around the development, sharing and using of TEL datasets for research.…”

“…Recently, the dataTEL project published an initial list of 20 available TEL datasets for research and compared the different datasets according to certain criteria (see Table 1) [46]. With this initiative the amount of available TEL datasets has increased and initial comparison study's are emerging that use the same dataset for different personalisation techniques [47] [48][49] [50]. The overall aim of the dataTEL initiative is to make different personalisation approaches more comparable to gain a body of knowledge about the effects of personalisation on learning.…”

Panorama of Recommender Systems to Support Learning

“…In 2011 most TEL recommender studies have still used rather small datasets which were not made public available [63] [64]. Since than the dataTEL Theme Team of the European network of excellence STELLAR [24] collected an initial set of datasets that can be used by the research community [109].…”

Intelligent Recovery Architecture for Personalized Educational Content

“…Researchers and developers of e-learning have begun to apply information retrieval techniques with technologies for recommendation, especially collaborative filtering [1], or web mining [2], for recommending educational content. A recent review of these applications can be seen in [3]. The features that handle these information filtering techniques in this context are the attribute information of education item (content-based approach) and the user context (collaborative approach).…”