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Type of publication: conference paper
Type of publication (PDB): Recenzuojamos išplėstinės tezės / Peer-reviewed extended theses (T1d)
Field of Science: Edukologija / Education (S007)
Author(s): Volungevičienė, Airina;Duart, Josep M;Tamoliūnė, Giedrė;Naujokaitienė, Justina
Title: Enhancing teacher decisions through learning analytics
Is part of: Towards personalized guidance and support for learning: 10th EDEN research workshop, Barcelona, Spain, 24-26 October 2018: conference proceedings / edited by Josep M. Duart, András Szűcs. Budapest : European distance and E-learning network, 2018
Extent: p. 158-160
Date: 2018
Keywords: Learning analytics;Online learning;Online teaching
ISBN: 9786155511257
Abstract: Learning analytics can be defined as the measurement and collection of extensive data about learners with the aim of understanding and optimising the learning process and environments in which it happens. In the recent decade researchers have started a fundamentally new direction of learning analytics by initially addressing big data (Picciano, 2012), educational data mining (Siemens & Baker, 2012), academic analytics, social learning and action analytics (Ferguson, 2012), as well as issues of student dropouts and ways of increasing student success (Arnold & Pistilli, 2012), with the purpose of developing a method of how learning analytics may enhance teaching and learning (Gasevic, Dawson, & Siemens, 2015). This shift revealed a completely new area of research in education with the prospect of reconsidering ways how learning analytics may contribute to better teaching and learning, addressing, in particular, issues in higher education (Zilvinskis & Borden, 2017) and massive open and online learning. The research implemented within the framework of the research project “Open and Online Learning for Digitalised and Networked Society” (project No. No. 09.3.3-LMT-K-712-01-0189) funded by the European Social Fund according to the activity “Improvement of researchers” qualification by implementing world-class R&D projects’ of Measure No. 09.3.3-LMT-K-712 focused on how learning analytics as a metacognitive tool can be applied for developing a learning analytics method for reflective teacher practice. [...]
Affiliation(s): Edukologijos tyrimų institutas
Vytauto Didžiojo universitetas
Švietimo akademija
Appears in Collections:3. Konferencijų medžiaga / Conference materials
Universiteto mokslo publikacijos / University Research Publications

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