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Pregled bibliografske jedinice broj: 823277

Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments


Gamulin, Jasna; Gamulin, Ozren; Kermek, Dragutin
Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments // Expert systems, 33 (2016), 2; 189-200 doi:10.111/exsy.12142 (međunarodna recenzija, članak, znanstveni)


Naslov
Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments

Autori
Gamulin, Jasna ; Gamulin, Ozren ; Kermek, Dragutin

Izvornik
Expert systems (0266-4720) 33 (2016), 2; 189-200

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

Ključne riječi
Educational data mining ; student performance prediction ; time series ; frequency domain ; discrete Fourier transforms

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove
Medicinski fakultet, Zagreb

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Uključenost u ostale bibliografske baze podataka:


  • INSPEC


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