his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Educational Data Mining: En kvalitativ studie med inriktning på dataanalys för att hitta mönster i närvarostatistik
University of Skövde, School of Informatics.
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesisAlternative title
Educational Data Mining : A qualitative study focusing on data analysis to find patterns in presence statistics (English)
Abstract [sv]

Studien fokuserar på att hitta olika mönster i närvarostatistik hos elever som inte närvarar i skolan. Informationen som resultatet ger kan därefter användas som ett beslutsunderlag för skolor eller till andra organisationer som är intresserade av EDM inom närvarostatistik.

Arbetet genomförde en kvalitativ metodansats med en fallstudie som bestod utav en litteraturstudie samt en implementation. Litteraturstudien användes för att få en förståelse över vanliga tillvägagångssätt inom EDM, som därefter låg till grund för implementationen som använde arbetssättet CRISP-DM.

Resultatet blev fem olika mönster som definieras genom dataanalys. Mönstren visar frånvaro ur ett tidsperspektiv samt per ämne och kan ligga till grund för framtida beslutsunderlag.

Abstract [en]

The study focuses on finding different patterns in attendance statistics for students who are not present at school. The information provided by the results can thereafter be used as a basis for decision-making for schools or for other organizations interested in EDM within attendance statistics.

The work carried out a qualitative method approach with a case study that consisted a literature study and an implementation. The literature study was used to gain an understanding of common approaches within EDM, which subsequently formed the basis for the implementation that used the working method CRISP-DM.

The project resulted in five different patterns defined by data analysis. The patterns show absence from a time perspective and per subject and can form the basis for future decision-making.

Place, publisher, year, edition, pages
2019. , p. 37
Keywords [en]
Data Mining, Educational Data Mining, Patterns
Keywords [sv]
Data Mining, Educational Data Mining, Mönster
National Category
Information Systems
Identifiers
URN: urn:nbn:se:his:diva-16987OAI: oai:DiVA.org:his-16987DiVA, id: diva2:1328732
Educational program
Information Systems - Business Intelligence
Supervisors
Examiners
Available from: 2019-06-24 Created: 2019-06-23 Last updated: 2019-06-24Bibliographically approved

Open Access in DiVA

fulltext(1268 kB)2 downloads
File information
File name FULLTEXT02.pdfFile size 1268 kBChecksum SHA-512
942e7ae803cf57c4dbc41b35afcccaa78cefc4b68f24b847aeff0fba57c43ce9ba277419f123361383c3c2ef10385607f32dfe272da2a304931511e4de90e1cb
Type fulltextMimetype application/pdf

By organisation
School of Informatics
Information Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 2 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 12 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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