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Facebook, the popular online social network, is used on daily basis by over 1 billion people each day. Its users use it for message exchange, sharing photos, publishing statuses etc. Recent research shows that a possibility exists of determining the connection level (or strength of their friendship, tie strength) between users based on analyzing their interaction on Facebook. The aim of this paper is to explore, as a proof of concept, the possibility of using a model for calculating strength of friendship to compare and classify ego-user’s Facebook friends. A survey, which involved more than 2500 people and collected a significant amount of data, was conducted through a developed web application. Analysis of collected data revealed that the model can determine with a high level of accuracy the stronger connections of ego-user and classify ego-user’s friends into several groups according to the estimated strength of their friendship. Conducted research is the base for creating an enriched social graph – graph which shows all kinds of relations between people and their intensity. Results of this research have plenty of potential uses, one of which is specifically improvement of the education process, especially in the segment of e- learning.

Unutar svake organizacije, pa tako i na visokoškolskim ustanovama, vrlo je bitno kvalitetno upravljati znanjem što omogućava prilagodbu novim okolnostima te stalna unapređenja različitih procesa i aktivnosti. Sustavi poslovne inteligencije omogućuju efikasnu sintezu i prenošenje znanja. Kao takvi omogućuju zaposlenicima na svim razinama unutar organizacije bolji uvid u čimbenike koji utječu na njihov segment poslovanja i donošenje boljih odluka. U radu je predstavljena teoretska podloga kreiranja organizacijskog znanja. Ona je isprepletena s cjelokupnim procesom dubinske analize podataka te povezana sa sustavima poslovne inteligencije, od faze njihovog planiranja do faze intenzivnog korištenja sustava. Značajni segmenti poslovne inteligencije su različite statističke analize i dubinska analiza podataka. U radu je prikazano kako se ove analize mogu iskoristiti za unapređenje specifičnog područja djelovanja visokoškolske ustanove – u nastavi. Poseban je naglasak dan na mogućnost unapređenja izvođenja nastave, poboljšanja testiranja studenata te potencijalnog povećanja prolaznosti bez snižavanja kriterija ocjenjivanja. Također su dani prijedlozi proširenja modela podataka koji bi omogućili otkrivanje novih zakonitosti i daljnja poboljšanja. For every organization, including universities, it is very important to efficiently govern with knowledge. This enables adoption to new circumstances and improvement in different processes and business related activities. Business intelligence systems enable efficient creation and transfer of knowledge. They enable employees at every organizational level to have better view of factors affecting their business and make better decisions. This thesis introduces theoretical background of organizational knowledge creation. Presented theory is interlaced with data mining process and further related to business intelligence systems, from their planning phase to phase of their extensive usage. Important segments of business intelligence are various statistical analysis same as data mining. This thesis presents how could these segments play important role within specific ‘business’ area at universities – improvement of teaching related activities. Focus has been put particularly on efficiency of the teaching process, objectivity at student’s work evaluation and potential improvement in student passing rate without reduction in grading criteria. At the end, proposals for data model extension were given, thus enabling better insight in real world rules same as further improvements.

Tema ovoga rada dubinska je analiza podataka vezanih uz edukaciju. U radu je predstavljena povijesna i teorijska pozadina područja dubinske analize podataka, istaknute su važne metode te je prikazana provedena analiza nad podacima iz područja edukacije. Podaci analizirani u sklopu ovoga rada, službeni su podaci Fakulteta elektrotehnike i računarstva. Provedeno je više analiza veza između uspješnosti različitih predmeta te su stvorena dva prediktivna modela za uspješnost studenta na matematičkom predmetu. This thesis focuses on the mining and analysis of educational data. The thesis presents the historical and theoretical background of educational data mining, highlights the most important and most common methods in that area, and conducts and presents an analysis of a specific educational dataset. The data analyzed in the scope of this thesis is the official dataset obtained from the Faculty of Electrical Engineering and Computing, University of Zagreb. Several different analyses were conducted as part of this work, including an analysis of the relationship between success for pairs of related courses, and the construction and training of two supervised models used for the prediction of student’s success in mathematical courses.