Primjena klasifikacijske analize u edukacijskoj domeni

undergraduate thesis

• Title (english): Application of Classification Analysis in the Educational Domain
• Author: Matteo Miloš
• Mentor: Damir Pintar (mentor)
• Committee member: Damir Pintar (predsjednik povjerenstva)
• Committee member: Mihaela Vranić (član povjerenstva)
• Committee member: Luka Humski (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2017-07-10, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U ovom radu obradili smo proces dubinske analize podataka u edukacijskoj domeni (EDM). Kroz prvih par poglavlja, fokusirali smo se na znanstvene discipline iz kojih je proizašao i čijim se metodama koristi EDM, a to su rudarenje podataka i strojno učenje. Također, objasnili smo i kako se razvijalo polje EDM-a te pristupe koji se koriste u analizi. U zadnjem poglavlju smo odradili praktični dio završnog rada, a to je primjena klasifikacijskih metoda kod dubinske analize podataka u edukacijskoj domeni. Analize smo proveli koristeći programski jezik R i dva klasifikacijska algoritma (k-NN algoritam i logistička regresija) te smo prikazali njihov način učenja, a u konačnici i rezultate (uspješnost učenja) tih algoritama.

Abstract (english)

In this thesis we have described the educational data mining process. Through the first few chapters, our main focus was on science fields which have preceded and whose methods are used in EDM, which are data mining and machine learning. Also, we have explained development of EDM and main approaches that are used in analysis. In the last chapter we did the practical part of this work, and that was application of classification methods in educational data mining. Analysis were conducted using programming language R and two classification algorithms (k-NN algorithm and logistic regression) and in those analysis we have shown in which way are these algorithms learning and results (level of success) which they produce.

• Keywords: dubinska analiza podataka u edukacijskoj domeni; otkrivanje znanja u skupovima podataka; strojno ucenje; klasifikacijska analiza; programski jezik R
• Keywords (english): educational data mining; knowledge discovery in databases; machine learning; classification analysis; programming language R
• Language: croatian
• URN:NBN: urn:nbn:hr:168:871012
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-03-13 23:10:59