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Scientific paper - Original scientific paper
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Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments
Gamulin, Jasna; Gamulin, Ozren; Kermek, Dragutin (2016)
Cite this item:
https://urn.nsk.hr/urn:nbn:hr:211:534638
Metadata
Language
English
Title (English)
Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments
Author
Gamulin, Jasna
Gamulin, Ozren
Kermek, Dragutin
Abstract (English)
In this work, it is shown that student access time series generated from Moodle log files contain information sufficient for successful prediction of student final results in blended learning courses. It is also shown that if time series is transformed into frequency domain, using discrete Fourier transforms (DFT), the information contained in it will be preserved. Hence, resulting periodogram and its DFT coefficients can be used for generating student performance models with the algorithms commonly used for that purposes. The amount of data extracted from log files, especially for lengthy courses, can be huge. Nevertheless, by using DFT, drastic compression of data is possible. It is experimentally shown, by means of several commonly used modelling algorithms, that if in average all but 5–10% of most intensive and most frequently used DFT coefficients are removed from datasets, the modelling with the remained data will result with the increase of the model accuracy. Resulting accuracy of the calculated models is in accordance with results for student performance models calculated for different dataset types reported in literature. The advantage of this approach is its applicability because the data are automatically collected in Moodle logs.
Keywords (English)
educational data mining
student performance prediction
time series
frequency domain
discrete Fourier transforms
Publication type
scientific paper - original scientific paper
Publication status
published
Peer review
peer review - international
Journal title
Expert systems
Numbering
2016, Vol. 33, No. 2, pp 189-200
ISSN
0266-4720
Date
publication: 2016.
DOI identifier
10.1111/exsy.12142
Article URL
http://bib.irb.hr/823277
http://onlinelibrary.wiley.com/doi/10.1111/exsy.v33.2/issuetoc
Scientific field
SOCIAL SCIENCES
Information and Communication Sciences
Institution
University of Zagreb, Faculty of Organization and Informatics Varaždin
URN:NBN
https://urn.nsk.hr/urn:nbn:hr:211:534638