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Sequence classification on gamified behavior data from a learning management system: Predicting student outcome using neural networks and Markov chain
University of Skövde, School of Informatics.
2020 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This study has investigated whether it is possible to classify time series data originating from a gamified learning management system. By using the school data provided by the gamification company Insert Coin AB, the aim was to distribute the teacher’s supervision more efficiently among students who are more likely to fail. Motivating this is the possibility that the student retention and completion rate can be increased. This was done by using Long short-term memory and convolutional neural networks and Markov chain to classify time series of event data. Since the classes are balanced the classification was evaluated using only the accuracy metric. The results for the neural networks show positive results but overfitting seems to occur strongly for the convolutional network and less so for the Long short-term memory network. The Markov chain show potential but further work is needed to mitigate the problem of a strong correlation between sequence length and likelihood.

Place, publisher, year, edition, pages
2020. , p. 26
Keywords [en]
Long Short-term Memory, Convolutional neural network, Markov Chain, Time series Classification, Gamification
National Category
Information Systems, Social aspects
Identifiers
URN: urn:nbn:se:his:diva-18654OAI: oai:DiVA.org:his-18654DiVA, id: diva2:1448016
External cooperation
Insert Coin AB
Subject / course
Informationsteknologi
Educational program
Data Science - Master’s Programme
Supervisors
Examiners
Available from: 2020-06-26 Created: 2020-06-26 Last updated: 2020-06-26Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • apa-cv
  • 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