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

Scientific paper - Original scientific paper

• Author: Jasna Gamulin
• Author: Ozren Gamulin; MBZ: 178974
• Author: Dragutin Kermek
• Author's institution: University 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... More 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. Less

• Keywords (english): educational data mining; student performance prediction; time series; frequency domain; discrete Fourier transforms
• Language: english
• Publication type: Scientific paper - Original scientific paper
• Publication status: Published
• Peer review: Peer review - international
• Publication version: Published version
• Journal title: Expert systems
• Numbering: vol. 33, no. 2, pp. 189-200
• p-ISSN: 0266-4720
• DOI: https://doi.org/10.1111/exsy.12142
• URN:NBN: urn:nbn:hr:211:534638
• Publication: 2016
• Document URL: http://bib.irb.hr/823277
• Document URL: http://onlinelibrary.wiley.com/doi/10.1111/exsy.v33.2/issuetoc
• Type of resource: Text
• Access conditions: Open access
• Created on: 2017-09-21 10:04:10