The learning registry

Bienkowski, Marie; Brecht, John; Klo, Jim

doi:10.1145/2330601.2330651

Cited by 17 publications

References 3 publications

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An ontology‐based framework of assessment analytics for massive learning

Nouira

Cheniti‐Belcadhi

Braham

2019

Comp Applic In Engineering

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Analyzing learning traces is presently highly required in e‐learning environment. Several communities have been developed to address this need, such as those of Learning Analytics and Educational Data Mining. The main step of performing a learning analytics process is the educational data collection. Actually, learning environments such as Massive Open Online Course (MOOC) generate a big amount of educational data. They can be divided into assessment data, collaboration data, communication data, and so on. When we focus on assessment, we can launch a new source of data that can be analyzed and hence contribute to the improvement of learning analytics field. In this paper, we explore, investigate and compare the set of learning analytics models in the literature. Then, we study them from assessment point of view. The only current learning analytics model which can support tracking and modeling assessment data is the xAPI data model. For this reason, we study and investigate the xAPI specification from assessment point of view. Based on identified weaknesses of xAPI specification, we propose an enhancement of its data model. This is to support the assessment analytics effectively. We present an ontological model for assessment analytics inspired from the xAPI specification. To validate our approach, we focus on massive learning traces extracted from a real MOOC. Thus, we define and execute the set of proposed steps of preprocessing stage that extracts assessment data from whole learning data. Furthermore, we develop a java semantic web application to convert assessment data extracted to OWL file according to our proposed ontological model for assessment analytics.

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An ontology‐based framework of assessment analytics for massive learning

Nouira

Cheniti‐Belcadhi

Braham

2019

Comp Applic In Engineering

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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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