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master's thesis
PREDIKTIVNI MODEL KONAČNOG ISHODA STUDENATA PRIMJENOM UMJETNIH NEURONSKIH MREŽA
diplomski rad
Split: University of Split, Faculty of economics Split, 2022. urn:nbn:hr:124:301746
Ljubičić, Teo
University of Split
Faculty of economics Split

Institutional repository: REFST

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Ljubičić, T. (2022). PREDIKTIVNI MODEL KONAČNOG ISHODA STUDENATA PRIMJENOM UMJETNIH NEURONSKIH MREŽA : diplomski rad (Master's thesis). Split: University of Split, Faculty of economics Split. Retrieved from https://urn.nsk.hr/urn:nbn:hr:124:301746

Ljubičić, Teo. "PREDIKTIVNI MODEL KONAČNOG ISHODA STUDENATA PRIMJENOM UMJETNIH NEURONSKIH MREŽA : diplomski rad." Master's thesis, University of Split, Faculty of economics Split, 2022. https://urn.nsk.hr/urn:nbn:hr:124:301746

Ljubičić, Teo. "PREDIKTIVNI MODEL KONAČNOG ISHODA STUDENATA PRIMJENOM UMJETNIH NEURONSKIH MREŽA : diplomski rad." Master's thesis, University of Split, Faculty of economics Split, 2022. https://urn.nsk.hr/urn:nbn:hr:124:301746

Ljubičić, T. (2022). 'PREDIKTIVNI MODEL KONAČNOG ISHODA STUDENATA PRIMJENOM UMJETNIH NEURONSKIH MREŽA : diplomski rad', Master's thesis, University of Split, Faculty of economics Split, accessed 11 July 2024, https://urn.nsk.hr/urn:nbn:hr:124:301746

Ljubičić T. PREDIKTIVNI MODEL KONAČNOG ISHODA STUDENATA PRIMJENOM UMJETNIH NEURONSKIH MREŽA : diplomski rad [Master's thesis]. Split: University of Split, Faculty of economics Split; 2022 [cited 2024 July 11] Available at: https://urn.nsk.hr/urn:nbn:hr:124:301746

T. Ljubičić, "PREDIKTIVNI MODEL KONAČNOG ISHODA STUDENATA PRIMJENOM UMJETNIH NEURONSKIH MREŽA : diplomski rad", Master's thesis, University of Split, Faculty of economics Split, Split, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:124:301746

Metadata
TitlePREDIKTIVNI MODEL KONAČNOG ISHODA STUDENATA PRIMJENOM UMJETNIH NEURONSKIH MREŽA : diplomski rad
AuthorTeo Ljubičić
MentorMarko Hell (mentor)
MBZ: 259274
GranterUniversity of Split
Faculty of economics Split
Split
Defense date and country2022-09, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Business Informatics
Abstract
Tehnološke promjene uzrokovane pandemijom COVID-19 dovele su do nagle digitalizacije obrazovanja gdje online nastava i platforme za učenje na daljinu koje postaju dio svakodnevnice. To je uzrokovalo gomilanje velike količine neiskorištenih podataka u bazama podataka obrazovnih institucija i time otvorilo pitanje potencijala rudarenja podataka u korist tih institucija. Pomoću umjetnih neuronskih mreža moguće je otkriti uzorke u podacima i dobiti uvid u ponašanje studenata na temelju kojih obrazovne institucije mogu donijeti bolje odluke. Unatoč tome što se umjetne neuronske mreže već naširoko koriste za rješavanje raznih problema, one su vrlo malo prisutne u obrazovanju. Stoga je cilj ovoga rada istražiti mogućnosti umjetnih neuronskih mreža u obrazovanju provođenjem istraživanja predikcije konačne uspješnosti studenata na kraju treće godine na temelju njihovih ocjena s prve godine studija. Istraživanje je provedeno na ukupno tri modela umjetnih neuronskih mreža, a kako bi se bolje donio zaključak, za usporedbu je korišten i model linearne regresije. Na temelju mjerila kvadratnog odstupanja vrijednosti, istraživanje je pokazalo da umjetne neuronske mreže mogu vrlo dobro predvidjeti konačnu uspješnost studenta gdje je po rezultatima nadmašilo čak i linearnu regresiju.
Abstract (english)
Technological changes caused by the COVID-19 pandemic have led to a sudden digitization of education, where online classes and distance learning platforms have become part of everyday life. This caused the accumulation of a large amount of unused data in the databases of educational institutions and thus opened the question of the potential of mining the data for the benefit of these institutions. With the use of artificial neural networks, it is possible to discover patterns in data that provide insight into student behavior upon which educational institutions are able to make better decisions. Despite the fact that artificial neural networks are already widely used to solve various problems, they are very little present in education. Therefore, the goal of this work is to investigate the possibilities of artificial neural networks in education by conducting research on the prediction of students' final performance at the end of the third year based on their grades from the first year of study. The research was conducted on a total of three models of artificial neural networks, and in order to reach a better conclusion, a linear regression model was also used for comparison. Based on the square deviation of the values, the research showed that artificial neural networks can very well predict the final performance of the student, where it even surpassed linear regression according to the results.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:124:301746
Study programmeTitle: Business Studies
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra ekonomije (magistar/magistra ekonomije)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-10-21 08:46:48