Search (4 results, page 1 of 1)

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Date

22. 2.2003 17:25:39
22. 2.2003 18:20:07

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Date

22. 1.2017 16:54:03
22. 1.2017 17:10:56

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Abstract

Along with significant opportunities, Massively Open Online Courses (MOOCs) provide major challenges to students (keeping track of course materials and effectively interacting with teachers and fellow students), teachers (managing thousands of students and supporting their learning progress), researchers (understanding how students interact with materials and each other), and MOOC platform developers (supporting effective course design and delivery in a scalable way). This article demonstrates the use of data analysis and visualization as a means to empower students, teachers, researchers, and platform developers by making large volumes of data easy to understand. First, we introduce the insight needs of different stakeholder groups. Second, we compare the wide variety of data provided by major MOOC platforms. Third, we present a novel framework that distinguishes visualizations by the type of questions they answer. We then review the state of the art MOOC visual analytics using a tabulation of stakeholder needs versus visual analytics workflow types. Finally, we present new data analysis and visualization workflows for statistical, geospatial, and topical insights. The workflows have been optimized and validated in the Information Visualization MOOC (IVMOOC) annually taught at Indiana University since 2013. All workflows, sample data, and visualizations are provided at http://cns.iu.edu/2016-MOOCVis.html.

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Abstract

Today, we attempt to access all humanity's knowledge and expertise using search engines such as Google. This works well for fact retrieval. However, search engines do not enlighten the user as to the inherent structure of the information being searched or give the user feedback as to its completeness. There is no 'up' button. The user is not able to see what dataset was queried, how the entries in a search result set relate to each other or how the retrieved entities relate to the entities that were not retrieved. Effective approaches to information access and management need to take into account the human user's perceptual and cognitive capabilities. Humanity is in true need of better tools to filter, navigate, understand, and utilize (scholarly) knowledge. This chapter discusses domain maps as an alternative means to organize, navigate, and internalize scholarly knowledge. We first discuss the educational uses of maps and the benefits of information visualization and spatialization for education. Subsequently, we introduce thematic maps, cognitive and concept maps, knowledge domain visualizations, and information spaces employing the metro map metaphor. All four are visual representations of geographic or abstract semantic spaces. Given that our interest is in the access, management, and internalization of scholarly knowledge, knowledge domain visualizations are discussed at greater length. To this end, we discuss how the educational use of knowledge domain visualizations is supported by the semantic network theory of learning. We also discuss some of the elements of good knowledge domain map design. These are drawn from visual perception principles and the study of human memory, and cognition. The final section projects a potential future of educational knowledge domain visualizations.