In this paper we propose an overview of Learning Analytics approaches in the context of Open education in its most recent developments related to Massive Open Online Courses (MOOCs) and Linked Data (LD), both strictly interconnected to the Open Educational Resources (OERs) concept. The MOOCs have developed as a seamless integration of different solutions and services which have appeared on the Web in recent years to enable the opening of courses to hundreds or thousands of participants on the Internet (Siemens 2013). Actually MOOCs combine, amongst others, the networking potentials offered by the social Web (developed around the Web 2.0 portals), the ease of access to OERs, the informal learning opportunities which characterize the learners' autonomy in identifying their own patterns to knowledge, thus changing the way learning occurs. The huge amount of data produced during a MOOC suggest that Learning Analytics techniques could be extremely useful to support the learning and teaching processes during the course. Nevertheless, there is not yet a substantial body of literature on the Learning Analytics of MOOCs (Clow, 2013). D'Aquin in (d'Aquin, 2012) has pointed out how the Linked Data can be exploited in the educational field. In fact, LD offers educational benefits for teaching and learning by providing integration mechanisms of heterogeneous resources from diverse repositories and sources and by supporting automatic search techniques that provide learners with educational resources semantically related to a specific knowledge field. As a consequence, the huge amount of datasets of LD can be exploited for educational purposes, thus becoming an alternative source of Open Educational resources. The recent evolution of LD and its datasets has had a remarkable influence on Learning Analytics as well (Gasevic, 2012), so that Learning Analytics is not limited to numeric data, but it has to take into account data and their semantic representation. The aim of this paper is therefore to highlight - through the analysis of the recent literature - how Learning Analytics can enhance learning experiences in Open Education settings based on MOOCs and LD.
Learning analytics in open education: an overview
Fulantelli Giovanni;Davide Taibi
2014
Abstract
In this paper we propose an overview of Learning Analytics approaches in the context of Open education in its most recent developments related to Massive Open Online Courses (MOOCs) and Linked Data (LD), both strictly interconnected to the Open Educational Resources (OERs) concept. The MOOCs have developed as a seamless integration of different solutions and services which have appeared on the Web in recent years to enable the opening of courses to hundreds or thousands of participants on the Internet (Siemens 2013). Actually MOOCs combine, amongst others, the networking potentials offered by the social Web (developed around the Web 2.0 portals), the ease of access to OERs, the informal learning opportunities which characterize the learners' autonomy in identifying their own patterns to knowledge, thus changing the way learning occurs. The huge amount of data produced during a MOOC suggest that Learning Analytics techniques could be extremely useful to support the learning and teaching processes during the course. Nevertheless, there is not yet a substantial body of literature on the Learning Analytics of MOOCs (Clow, 2013). D'Aquin in (d'Aquin, 2012) has pointed out how the Linked Data can be exploited in the educational field. In fact, LD offers educational benefits for teaching and learning by providing integration mechanisms of heterogeneous resources from diverse repositories and sources and by supporting automatic search techniques that provide learners with educational resources semantically related to a specific knowledge field. As a consequence, the huge amount of datasets of LD can be exploited for educational purposes, thus becoming an alternative source of Open Educational resources. The recent evolution of LD and its datasets has had a remarkable influence on Learning Analytics as well (Gasevic, 2012), so that Learning Analytics is not limited to numeric data, but it has to take into account data and their semantic representation. The aim of this paper is therefore to highlight - through the analysis of the recent literature - how Learning Analytics can enhance learning experiences in Open Education settings based on MOOCs and LD.File | Dimensione | Formato | |
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