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Visualization of Learning Paths as Networks of Topics
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2020 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Nowadays, interactive visualizations have been one of the most used tools in Big Data fields for the purpose of searching for relationships and structured information in large datasets of unstructured information. In this project, these tools are applied to extract structured information from students following Self-Regulated Learning (SRL). By means of an interactive graph, we are able to study the paths that the students follow in the learning materials. Our visualization supports the investigation of patterns of behaviour of the students, which later could be used, for example, to adapt the study program to the student’s needs in a dynamic way or offer guidance if necessary.

Place, publisher, year, edition, pages
2020. , p. 32
Keywords [en]
Visual Learning Analytics, Exploratory Data Analysis, Multidi- mensional Data, Interactive Graphs
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-100683OAI: oai:DiVA.org:lnu-100683DiVA, id: diva2:1523435
Subject / course
Computer Science
Presentation
2020-09-30, 13:19 (English)
Supervisors
Examiners
Available from: 2021-01-28 Created: 2021-01-28 Last updated: 2021-01-28Bibliographically approved

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fulltext(3323 kB)264 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf