Learning with Data: Visualization to Support Teaching, Learning, and Assessment
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Within the established paradigm of learning analytics, this special issue has a specific focus upon factors of learning associated with interactive data visualizations. Learning analytics use dynamic information about learners and learning environments, assessing, eliciting and analysing it, for real-time modelling, prediction, and optimization of learning processes, learning environments, and educational decision-making (Ifenthaler 2015). Opportunities of learning analytics are fostering interactions between students and facilitators as well as the availability of personalised and adaptive help and feedback from peer learners in near real-time (Ifenthaler and Widanapathirana 2014). The advent of big data requires new perspectives on data processing and analysis including advanced methods and tools to visualise data for supporting learning processes. The primary purpose of visualisations of big data is to communicate complex patterns nested in big data (Chen 2010). However, not only the visualisation of numeric information using dashboards are important areas of research. The visualisation of semantic content are an emerging field of research opening up new perspectives on natural language processing and in-depth analysis of text data (Ifenthaler 2014 ; Ifenthaler and Pirnay-Dummer 2014). This special issue of Technology, Knowledge and Learning features articles which showcase the latest advances in the field of data visualisation for learning.
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Tsui, Chi-Yan (2003)This study investigated the secondary school students' learning of genetics when their teachers included an interactive computer program BioLogica in classroom teaching and learning. Genetics is difficult to teach and ...
A web service service for the dynamic linkage and visualisation of multivariate spatiotemporal informationMoncrieff, Simon; West, Geoff (2013)In spatial health research, it is necessary to not only consider the spatial and temporal distributions of diseases, but also external factors that influence the disease, such as environmental and socio-economic factors. ...
Pearce, Adrian (1996)Spatial interpretation involves the intelligent processing of images for learning, planning and visualisation. This involves building systems which learn to recognise patterns from the content of unconstrained data such ...