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Sequence classification on gamified behavior data from a learning management system: Predicting student outcome using neural networks and Markov chain
Högskolan i Skövde, Institutionen för informationsteknologi.
2020 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
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.

sted, utgiver, år, opplag, sider
2020. , s. 26
Emneord [en]
Long Short-term Memory, Convolutional neural network, Markov Chain, Time series Classification, Gamification
HSV kategori
Identifikatorer
URN: urn:nbn:se:his:diva-18654OAI: oai:DiVA.org:his-18654DiVA, id: diva2:1448016
Eksternt samarbeid
Insert Coin AB
Fag / kurs
Informationsteknologi
Utdanningsprogram
Data Science - Master’s Programme
Veileder
Examiner
Tilgjengelig fra: 2020-06-26 Laget: 2020-06-26 Sist oppdatert: 2020-06-26bibliografisk kontrollert

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