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Title:ANALIZA LASTNOSTI USPEŠNIH ŠTUDENTOV S PODATKOVNIM RUDARJENJEM
Authors:ID Kompara, Marko (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
Files:.pdf UNI_Kompara_Marko_2012.pdf (3,02 MB)
MD5: 57752F78B94C4AA1739405B02963DF08
PID: 20.500.12556/dkum/e2193b31-c860-4e3f-8b12-7cbeb8119dcd
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V nalogi poskušamo odkriti povezave med lastnostmi študentov in njihovim študijskim uspehom. Opisan je celoten proces zbiranja ter analize podatkov in relacij, ki vplivajo na študijski uspeh posameznika. Začnemo s kratkimi navodili o sestavi ankete in nadaljujemo s sestavo svoje. Pridobljene podatke najprej analiziramo z uporabo statistike. Uporabljene metode na kratko opišemo in jih nato izvedemo nad zbranimi podatki. Proces izvajanja, ki je opravljen v programu IBM SPSS Statistics, je tudi predstavljen. Nazadnje uporabimo podatke v podatkovnem rudarjenju. V predstavitvi podatkovnega rudarjenja se posebej posvetimo nalogi klasifikacije in njenim tehnikam. V orodju Weka z odločitvenimi drevesi ugotavljamo povezanost med karakteristikami študentov in njihovim študijskim uspehom.
Keywords:podatkovno rudarjenje, statistična analiza, SPSS, Weka
Place of publishing:Maribor
Publisher:[M. Kompara]
Year of publishing:2012
PID:20.500.12556/DKUM-37783 New window
UDC:004.6(043.2)
COBISS.SI-ID:16504598 New window
NUK URN:URN:SI:UM:DK:CLQ1X35M
Publication date in DKUM:27.11.2012
Views:1860
Downloads:441
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:ANALYZING STUDENTS' SUCCESS FACTORS WITH DATA MINING
Abstract:In the assignment we try to discover correlations between students’ characteristics and their academic success. We describe the whole process of collecting and analyzing data and relations which impact the students’ success. We start with short instructions on how to build a survey and continuing to build our own. Collected data is first analyzed with statistics. Used methods are shortly described and afterwards executed in a program called IBM SPSS Statistics. Finally the data is used in data mining, presentation of which is mainly focused on the classification task and its techniques. Using Weka we built decision trees to find out if there are any connections between students’ characteristics and their study success.
Keywords:data mining, statistical analysis, SPSS, Weka


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