prikaz prve stranice dokumenta Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments
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Scientific paper - Original scientific paper
Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments
Expert systems, 33 (2016), 2; 189-200. https://doi.org/10.1111/exsy.12142
Gamulin, Jasna; Gamulin, Ozren; Kermek, Dragutin

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Gamulin, J., Gamulin, O. & Kermek, D. (2016). Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments. Expert systems, 33. (2), 189-200. doi: 10.1111/exsy.12142

Gamulin, Jasna, et al. "Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments." Expert systems, vol. 33, no. 2, 2016, pp. 189-200. https://doi.org/10.1111/exsy.12142

Gamulin, Jasna, Ozren Gamulin and Dragutin Kermek. "Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments." Expert systems 33, no. 2 (2016): 189-200. https://doi.org/10.1111/exsy.12142

Gamulin, J., Gamulin, O. and Kermek, D. (2016) 'Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments', Expert systems, 33(2), pp. 189-200. doi: 10.1111/exsy.12142

Gamulin J, Gamulin O, Kermek D. Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments. Expert systems [Internet]. 2016 [cited 2024 November 21];33(2):189-200. doi: 10.1111/exsy.12142

J. Gamulin, O. Gamulin and D. Kermek, "Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments", Expert systems, vol. 33, no. 2, pp. 189-200, 2016. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:211:534638. [Accessed: 21 November 2024]

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Metadata
Title (english)Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments
AuthorJasna Gamulin
AuthorOzren Gamulin
MBZ: 178974
AuthorDragutin Kermek
Author's institutionUniversity of Zagreb
Faculty of Organization and Informatics
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Information and Communication Sciences
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)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleExpert systems
Numberingvol. 33, no. 2, pp. 189-200
p-ISSN0266-4720
DOIhttps://doi.org/10.1111/exsy.12142
URN:NBNurn:nbn:hr:211:534638
Publication2016
Document URLhttp://bib.irb.hr/823277
Document URLhttp://onlinelibrary.wiley.com/doi/10.1111/exsy.v33.2/issuetoc
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2017-09-21 10:04:10