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master's thesis
Predviđanje odustajanja studenata od studija
2024. urn:nbn:hr:166:980254
Ercegović, Tea
University of Split
Faculty of Science

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Ercegović, T. (2024). Predviđanje odustajanja studenata od studija (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:980254

Ercegović, Tea. "Predviđanje odustajanja studenata od studija." Master's thesis, University of Split, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:166:980254

Ercegović, Tea. "Predviđanje odustajanja studenata od studija." Master's thesis, University of Split, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:166:980254

Ercegović, T. (2024). 'Predviđanje odustajanja studenata od studija', Master's thesis, University of Split, Faculty of Science, accessed 20 November 2024, https://urn.nsk.hr/urn:nbn:hr:166:980254

Ercegović T. Predviđanje odustajanja studenata od studija [Master's thesis]. Split: University of Split, Faculty of Science; 2024 [cited 2024 November 20] Available at: https://urn.nsk.hr/urn:nbn:hr:166:980254

T. Ercegović, "Predviđanje odustajanja studenata od studija", Master's thesis, University of Split, Faculty of Science, Split, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:166:980254

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Metadata
TitlePredviđanje odustajanja studenata od studija
Title (english)Student dropout prediction
AuthorTea Ercegović
MentorAni Grubišić (mentor)
Committee memberAni Grubišić (predsjednik povjerenstva)
Committee memberBranko Žitko (član povjerenstva)
Committee memberInes Šarić-Grgić (član povjerenstva)
GranterUniversity of Split
Faculty of Science
Split
Defense date and country2024-09-30, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Rad se bavi predviđanjem odustajanja studenata od studija pomoću različitih modela strojnog učenja kao što su logistička regresija, stabla odluke, random forest, SVM i gradient boosting. U analizu su uključeni podaci poput godine upisa, godine rođenja, spola, kategorija srednje škole, prosjeka ocjena u srednjoj školi, rezultata na državnoj maturi te uspjeha u pojedinim kolegijima tijekom studija.
Abstract (english)
This paper is concentrated on the prediction of students dropping out of studies using different machine learning models such as logistic regression, decision trees, random forest, SVM and gradient boosting. The analysis includes data such as year of enrollment, year of birth, gender, high school category, grade point average in high school, results at the state matriculation exam, and success in individual courses during studies.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:980254
Study programmeTitle: Computer Science; specializations in: specialization in Education
Course: specialization in Education
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni/a magistar/magistra edukacije informatike (sveučilišni/a magistar/magistra edukacije informatike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-10-24 13:01:22