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000234555 005__ 20190317000917.0
000234555 037__ $$aARTICLE 000234555 245__$$aSpecial issue on: Learning Analytics (Editorial)
000234555 260__ $$c2013 000234555 269__$$a2013
000234555 336__ $$aJournal Articles 000234555 520__$$aWith the general technological advances of the recent years, current learning environments amass an abundance of data. Albeit such data offer the chance of better understand the learning process, stakeholders – learners, teachers and institutions – often need additional support to make sense of it (Dyckhoff et al., 2013; Macfadyen and Dawson, 2012). The acknowledgement of these needs is at the heart of the recent emergence of Learning Analytics (LA), a research area that draws from multiple disciplines such as educational science, information and computer science, sociology, psychology, statistics and educational data mining (Buckingham Shum and Ferguson, 2012). This multidisciplinarity in LA has motivated the work done by Ferguson (2012), which provides a first review of the drivers, development and challenges of this novel and young research area. Our understanding of learning analytics is based on the definition from the Society for Learning Analytics (SoLAR – Society for Learning Analytics1) which specifies that “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. Since 2011, the Horizon reports list Learning Analytics as a hot topic in higher education and indicate the importance of data for this field (Johnson et al., 2011). Learning analytics are able to provide a fresh view on understanding of teaching and learning by observing patterns of complex data (Johnson et al., 2012). Furthermore, it will influence the evolution of higher education in a great measure. Nowadays, learners have access to a huge amount of online information having themselves the possibility of being content creators and information sharers. Therefore the quantity of available information grows in an exponential way, once that each and every citizen can access and produce information. For these purposes, learners have at their disposal many online resources, including LMSs, VLEs, MOOCs and many other online tools that facilitate the learning process and the development of competences. Taking into account these online learning facilities and therefore the learners’ acquisition of knowledge, it is also easier to measure and analyse their experiences by using learning analytics tools. Different online courses and institutions provide dashboards with information about student experiences, flaws and successes. Although the investigation of behavioural specific data makes learning analytics complex, the time comes to utilise personalised learning environments adapted to students learning paths, skills, previous knowledge, competences and motivation.
000234555 6531_ $$aLearning Analytics 000234555 700__$$aRivera-Pelayo, Verónica
000234555 700__ $$0248291$$g248809$$aRodriguez Triana, Maria Jesus 000234555 700__$$aPetrushyna, Zinayida
000234555 700__ $$aBraun, Simone 000234555 700__$$aLoureiro, Ana
000234555 700__ $$aKawase, Ricardo 000234555 773__$$j5$$tInternational Journal on Technology Enhanced Learning$$k2$$q97-106 000234555 85641$$uhttps://www.inderscience.com/info/dl.php?filename=2013/ijtel-3768.pdf$$yOfficial version 000234555 8564_$$uhttps://infoscience.epfl.ch/record/234555/files/IJTEL50200_Editorial.pdf$$zPublisher's version$$s178596$$yPublisher's version 000234555 909C0$$0252735$$pSTI 000234555 909CO$$particle$$ooai:infoscience.tind.io:234555$$qGLOBAL_SET$$pSTI 000234555 917Z8$$x248809
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000234555 980__ aARTICLE