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Pregled bibliografske jedinice broj: 778121

Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model


Đurđević Babić, Ivana
Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model // Croatian operational research review, 6 (2015), 1; 105-120 doi:10.17535/crorr.2015.0009 (podatak o recenziji nije dostupan, članak, znanstveni)


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Naslov
Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model

Autori
Đurđević Babić, Ivana

Izvornik
Croatian operational research review (1848-0225) 6 (2015), 1; 105-120

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
classification ; neural networks ; classification tree ; course satisfaction ; log data

Sažetak
In academic institutions students‘ course satisfaction has become an important issue over the years and is recognized as a support in ensuring effective and quality education as well as in enhancing students‘ studying experience. The aim of this paper is to investigate whether there is a connection between students‘ course log data in virtual learning environment (VLE) such as Moodle and students‘ course satisfaction. Furthermore, the paper explores whether is it possible to develop a successful classification model in order to predict student‘s course satisfaction based on their course log data. The research was conducted at the Faculty of Teacher Education in Osijek at Croatian University and included the analysis of log data and course satisfaction on the sample of third and fourth year students. For each study year log data within one VLE course and satisfaction with that course were obtained. Multilayer Perceptron (MLP) with different activation functions and Radial Basis Function (RBF) neural networks as well as classification tree models were developed, trained and tested in order to classify students into one of two categories of course satisfaction. For the purpose of model comparison, classification accuracy, type I and type II errors and input variable importance were used. The results indicate that MLP neural network model provides the highest average classification accuracy, although t-test of the difference in proportions showed that the difference in performance between the compared models is not statistically significant on the level of significance 0.05.

Izvorni jezik
Engleski

Znanstvena područja
Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove:
Fakultet za odgojne i obrazovne znanosti, Osijek

Profili:

Avatar Url Ivana Đurđević Babić (autor)

Citiraj ovu publikaciju

Đurđević Babić, Ivana
Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model // Croatian operational research review, 6 (2015), 1; 105-120 doi:10.17535/crorr.2015.0009 (podatak o recenziji nije dostupan, članak, znanstveni)
Đurđević Babić, I. (2015) Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model. Croatian operational research review, 6 (1), 105-120 doi:10.17535/crorr.2015.0009.
@article{article, author = {\DJur\djevi\'{c} Babi\'{c}, I.}, year = {2015}, pages = {105-120}, DOI = {10.17535/crorr.2015.0009}, keywords = {classification, neural networks, classification tree, course satisfaction, log data}, journal = {Croatian operational research review}, doi = {10.17535/crorr.2015.0009}, volume = {6}, number = {1}, issn = {1848-0225}, title = {Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model}, keyword = {classification, neural networks, classification tree, course satisfaction, log data} }
@article{article, author = {\DJur\djevi\'{c} Babi\'{c}, I.}, year = {2015}, pages = {105-120}, DOI = {10.17535/crorr.2015.0009}, keywords = {classification, neural networks, classification tree, course satisfaction, log data}, journal = {Croatian operational research review}, doi = {10.17535/crorr.2015.0009}, volume = {6}, number = {1}, issn = {1848-0225}, title = {Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model}, keyword = {classification, neural networks, classification tree, course satisfaction, log data} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • EconLit


Uključenost u ostale bibliografske baze podataka:


  • CompactMath
  • Current Index to Statistics
  • Current Mathematical Publications
  • DOAJ
  • EBSCO host
  • Genamics Journal Seek database
  • HRČAK
  • ProQuest


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