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Yuchen Liu; Stanislav Pozdniakov; Roberto Martinez-Maldonado – Australasian Journal of Educational Technology, 2024
Learning analytics (LA) dashboards are becoming increasingly available in various learning settings. However, teachers may face challenges in understanding and interpreting the data visualisations presented on those dashboards. In response to this, some LA researchers are incorporating visual cueing techniques, like data storytelling (DS), into LA…
Descriptors: Visualization, Story Telling, Data Use, Cognitive Processes
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Mejia, Carolina; Florian, Beatriz; Vatrapu, Ravi; Bull, Susan; Gomez, Sergio; Fabregat, Ramon – IEEE Transactions on Learning Technologies, 2017
Existing tools aim to detect university students with early diagnosis of dyslexia or reading difficulties, but there are not developed tools that let those students better understand some aspects of their difficulties. In this paper, a dashboard for visualizing and inspecting early detected reading difficulties and their characteristics, called…
Descriptors: Clinical Diagnosis, Dyslexia, Visualization, Metacognition
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O'Reilly, Una-May; Veeramachaneni, Kalyan – Research & Practice in Assessment, 2014
Because MOOCs bring big data to the forefront, they confront learning science with technology challenges. We describe an agenda for developing technology that enables MOOC analytics. Such an agenda needs to efficiently address the detailed, low level, high volume nature of MOOC data. It also needs to help exploit the data's capacity to reveal, in…
Descriptors: Data, Technology Uses in Education, Online Courses, Open Education
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Martinez, Michael E.; Peters Burton, Erin E. – Educational Media International, 2011
The "cyberinfrastucture" is a broad informational network that entails connections to real-time data sensors as well as tools that permit visualization and other forms of analysis, and that facilitates access to vast scientific databases. This multifaceted network, already a major boon to scientific discovery, now shows exceptional promise in…
Descriptors: Visualization, Educational Technology, STEM Education, Cognitive Processes
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Chow, Meyrick; Chan, Lawrence – Computers & Education, 2010
Information technology (IT) has the potential to improve the clinical learning environment. The extent to which IT enhances or detracts from healthcare professionals' role performance can be expected to affect both student learning and patient outcomes. This study evaluated nursing students' satisfaction with a novel compartmental Picture…
Descriptors: Nursing Students, Nursing Education, Visualization, Biomedicine
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Rafi, Ahmad; Samsudin, Khairulanuar – British Journal of Educational Technology, 2009
An experimental study involving 30 undergraduates (mean age = 20.5 years) in mental rotation (MR) training was conducted in an interactive Desktop Mental Rotation Trainer (iDeMRT). Stratified random sampling assigned students into one experimental group and one control group. The former trained in iDeMRT and the latter trained in conventional…
Descriptors: Experimental Groups, Control Groups, Computer Assisted Instruction, Statistical Analysis
Johnson, L.; Adams Becker, S.; Cummins, M.; Freeman, A.; Ifenthaler, D.; Vardaxis, N. – New Media Consortium, 2013
The "Technology Outlook Australian Tertiary Education 2013-2018: An NMC Horizon Project Regional Analysis" reflects a collaborative research effort between the New Media Consortium (NMC) and Open Universities Australia to help inform Australian educational leaders about significant developments in technologies supporting teaching,…
Descriptors: Foreign Countries, Educational Policy, Higher Education, Consortia
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Botturi, Luca – Australasian Journal of Educational Technology, 2004
This paper introduces the Quail Model, a device for the classification and visualisation of learning goals. The model is a communication tool that can smoothen the discussion within a course design team, support shared understanding, and improve decision making. Its theoretical background mingles contributions from instructional design (Bloom,…
Descriptors: Instructional Design, Models, Classification, Visualization