EuroVA14
https://diglib.eg.org:443/handle/10.2312/7746
ISBN 978-3-905674-68-22024-03-28T22:03:38ZTowards more Visual Analytics in Learning Analytics
https://diglib.eg.org:443/handle/10.2312/eurova.20141147.061-065
Towards more Visual Analytics in Learning Analytics
Ritsos, Panagiotis D.; Roberts, Jonathan C.
M. Pohl and J. Roberts
Learning Analytics is the collection, management and analysis of students' learning. It is used to enable teachers to understand how their students are progressing and for learners to ascertain how well they are performing. Often the data is displayed through dashboards. However, there is a huge opportunity to include more comprehensive and interactive visualizations that provide visual depictions and analysis throughout the lifetime of the learner, monitoring their progress from novices to experts. We therefore encourage researchers to take a comprehensive approach and re-think how visual analytics can be applied to the learning environment, and develop more interactive and exploratory interfaces for the learner and teacher.
2014-01-01T00:00:00ZSupporting an Early Detection of Diabetic Neuropathy by Visual Analytics
https://diglib.eg.org:443/handle/10.2312/eurova.20141145.049-053
Supporting an Early Detection of Diabetic Neuropathy by Visual Analytics
Luboschik, Martin; Röhlig, Martin; Kundt, Günther; Stachs, Oliver; Peschel, Sabine; Zhivov, Andrey; Guthoff, Rudolf F.; Winter, Karsten; Schumann, Heidrun
M. Pohl and J. Roberts
In this paper, we describe a step-wise approach to utilize ophthalmic markers for detecting early diabetic neuropathy (DN), the most common long-term complication of diabetes mellitus. Our approach is based on the Visual Analytics Mantra: First, we statistically analyze the data to identify those variables that separate DN patients from a control group. Afterwards, we show the important separating variables individually, but also in the context of all variables regarding a pre-defined classification. By doing so, we support the understanding of the categorization in respect of the value distribution of variables. This allows for zooming, filtering and further analysis like deleting non-relevant variables that do not contribute to the definition of markers as well as deleting data records with false data values or false classifications. Finally, outliers are observed and investigated in detail. So, a third group of potential DN patients can be introduced. In this way, the detection of early DN can be effectively supported.
2014-01-01T00:00:00ZInteractively Visualizing Summaries of Rules and Exceptions
https://diglib.eg.org:443/handle/10.2312/eurova.20141146.055-059
Interactively Visualizing Summaries of Rules and Exceptions
Sharma, Geetika; Shroff, Gautam; Pandey, Aditeya; Agarwal, Puneet; Srinivasan, Ashwin
M. Pohl and J. Roberts
Rules along with their exceptions have been used to explain large data sets in a comprehensible manner. In this paper we describe an interactive visualization scheme for rules and their exceptions. Our visual encoding is based on principles for creating perceptually effective visualizations from literature. Our visualization scheme presents an overview first, allows semantic zooming and then shows details on demand using established principles of interactive visualization. We assume that rules and exceptions have been mined and summarized using available techniques; however our visualization is applicable for more general rule hierarchies as well. We illustrate our visualization using rules and exceptions extracted from real customer surveys as well as on rule sets derived from past literature.
2014-01-01T00:00:00ZIntegrated Visualization and Analysis of a Multi-scale Biomedical Knowledge Space
https://diglib.eg.org:443/handle/10.2312/eurova.20141141.025-029
Integrated Visualization and Analysis of a Multi-scale Biomedical Knowledge Space
Agibetov, Asan; Vaquero, Ricardo Manuel Millan; Friese, Karl-Ingo; Patane, Giuseppe; Spagnuolo, Michela; Wolter, Franz-Erich
M. Pohl and J. Roberts
The study and analysis of relationships in a complex and multi-scale data set is a challenge of information and scientific visualization. This work proposes an integrated visualization to capture all the important aspects of multi-scale data into the same view by leveraging the multi-scale biomedical knowledge encoded into an underlying ontology. Ontology supports visualization by providing semantic means to identify relevant items that must be presented to the user. The study and analysis of relationships across the scales are presented as results of queries to the multi-scale biomedical knowledge space. We demonstrate the prototype of the graphical interface of an integrated visualization framework and the knowledge formalization support in an example scenario related to the musculoskeletal diseases.
2014-01-01T00:00:00Z