Issues and considerations regarding sharable data sets for recommender systems in technology enhanced learning

Drachsler, Hendrik; Bogers, Toine; Vuorikari, Riina; Verbert, Katrien; Duval, Erik; Manouselis, Nikos; Beham, Guenter; Lindstaedt, Stephanie; Stern, Hermann; Friedrich, Martin; Wolpers, Martin

doi:10.1016/j.procs.2010.08.010

Cited by 58 publications

(40 citation statements)

References 9 publications

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“…The research community around TEL RecSys is continuously growing as an increasing amount of research projects, conferences, workshops, special issues in journals and books shows. Examples include the Workshop series of Social Information Retrieval for Technology Enhanced Learning (SIRTEL 2007(SIRTEL -2009), the RecSysTEL Workshop series on Recommender Systems for Technology Enhanced Learning [64] [67], the dataTEL workshop series on datasets for Technology Enhanced Learning [24][25], a specific track on Recommender Systems for Learning (ReSyL) at the 14th IEEE International Conference on Advanced Learning Technologies (ICALT 2014) [26], the data competitions from 2013 until 2014 of the LinkedUp project [20] [18], as well as several special volumes of journals and books [112][108] [85][86] [103]. The diversity of the events over the years shows how relevant the research topics and challenges are for the TEL community.…”

Section: Introductionmentioning

confidence: 99%

“…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]. These days we see many more studies that take advantage of this initial collection of datasets to start their research [30].…”

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confidence: 99%

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Panorama of Recommender Systems to Support Learning

Drachsler

Verbert

Santos

et al. 2015

Recommender Systems Handbook

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191

115

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Panorama of Recommender Systems to Support Learning

Drachsler

Verbert

Santos

et al. 2015

Recommender Systems Handbook

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191

115

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“…In particular, we expect LD to facilitate TEL community with relevant datasets in order to gain more knowledge about personalisation of learning and build better recommender systems. So far the outcomes of different recommender systems and personalisation approaches in the educational domain are hardly comparable due to the diversity of algorithms, learner's models, datasets and evaluation criteria [43]. A kind of reference dataset is needed for the TEL recommender systems field, as is the MovieLens dataset 5 in the e-commerce field.…”

Section: Introductionmentioning

confidence: 99%

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

Dietze

Drachsler

Giordano

2014

Recommender Systems for Technology Enhanced Learning

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Abstract. Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are applied 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, metadata about TEL resources as well as users. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. The potential of LD to facilitate TEL recommender systems research and practice is discussed in this paper. In particular, an overview of most relevant LD sources and techniques is provided, together with a discussion of their potential for the TEL domain in general and TEL recommender systems in particular. Results from highly related European projects are presented and discussed together with an analysis of prevailing challenges and preliminary solutions.Keywords. Linked Data, Education, Semantic Web, Technology-Enhanced Learning, Data Consolidation, Data Integration IntroductionAs personalisation, adaptation and recommendation are central features of TEL environments, TEL recommender systems apply information retrieval techniques to filter and deliver learning resources according to user preferences and requirements. While the suitability and scope of possible recommendations is fundamentally dependent on the quality and quantity of available data, e.g., data about learners, and in particular metadata about TEL resources, the landscape of standards and approaches currently exploited to share and reuse educational data is highly fragmented.

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“…A challenge articulated by several researchers is the collection of datasets that can be shared for research purposes (Drachsler et al, 2010;Verbert, Manouselis, Drachsler, Duval, 2012). The overall objective is to collect data captured in real-life settings, from different learning environments, and to make such data available for researchers to enable comparison of research results.…”

Section: Challengesmentioning

confidence: 99%

Analysis and Reflections on the Third Learning Analytics and Knowledge Conference (LAK 2013)

et al. 2014

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Analyzing a conference, especially one as young and focused as LAK, provides the opportunity to observe the structure and contributions of the scientific community around it. This work will perform a Scientometric analysis, coupled with a more in-depth manual content analysis, to extract this insight from the proceedings and program of LAK 2013. Authorship analysis reveals an open and international community, while internal citation analysis provides evidence of the construction of a body of knowledge central to Learning Analytics. The analysis of the content of the papers found five main topics: visualization, behaviour analysis, social learning analytics, learning analytics for MOOCs, and learning analytics issues (ethical, scalability, etc.), as well as papers reflecting on the field itself. We discuss representative papers presented at the conference, highlighting trends and new developments. Learning analytics is a diverse multidisciplinary field with an emerging interdisciplinary core, well situated to benefit from productive dialogue concerning its scope and purpose and to reflect on the pedagogies and epistemologies implied by its methods.

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Issues and considerations regarding sharable data sets for recommender systems in technology enhanced learning

Cited by 58 publications

References 9 publications

Panorama of Recommender Systems to Support Learning

Panorama of Recommender Systems to Support Learning

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

Analysis and Reflections on the Third Learning Analytics and Knowledge Conference (LAK 2013)

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