Please use this identifier to cite or link to this item: https://rfos.fon.bg.ac.rs/handle/123456789/1976
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dc.creatorPetrović, Nikola
dc.creatorAnđelković Labrović, Jelena
dc.date.accessioned2023-05-12T11:23:42Z-
dc.date.available2023-05-12T11:23:42Z-
dc.date.issued2019
dc.identifier.issn0354-5415
dc.identifier.urihttps://rfos.fon.bg.ac.rs/handle/123456789/1976-
dc.description.abstractTehnike za otkrivanje zakonitosti u velikim bazama podataka primenjuju se već dugo na podatke proizašle iz obrazovnog procesa u privatnom i javnom sektoru. Upravo se na takve zakonitosti oslanjaju stručnjaci u upravljanju učenjem kada kreiraju i sprovode strategiju kojom se ostvaruju ciljevi učenja. U radu je izložen okvir za analitiku u učenju Grelera i Dašlera sa identifikovanim ključnim faktorima koje treba uzeti u obzir prilikom planiranja i sprovođenja analize. Njemu je pridružen okvir Knuda Ilerisa za "sveobuhvatan pristup učenju" kako bi se utvrdilo da li su saglasni po pitanju značaja određenih faktora i načina na koji ih predstavljaju. Uporednom analizom dva okvira identifikovane su mogućnosti upravljanja učenjem na osnovu podataka, potencijalni izazovi i ograničenja. Društveno okruženje, emotivna komponenta i otpori i odbrane u okviru za "sveobuhvatan pristup učenju" uvaženi su na višem nivou složenosti, te je za pristup zasnovan na podacima izazov da koriguje svoje razumevanje tih faktora.sr
dc.description.abstractData mining techniques have long been applied to data collected from the education process in the private and public sectors. Learning management professionals take the discovered patterns in consideration when they create and implement a strategy to achieve the learning goals. Greller and Drachsler framework for learning analytics was introduced with identified key factors that need to be considered during planning and conducting analysis. The comprehensive learning theory by Knud Illeris was also presented to determine whether they agree on the importance of certain factors and the way they represent them. A comparative analysis of the two frameworks identified the possibilities of data driven learning management, possible challenges and limitations. Social environment, emotional component, resistance and defense were understood at a higher level of complexity within the comprehensive learning framework while data driven approach is challenged to improve understanding of these factors.en
dc.publisherUniverzitet u Beogradu - Filozofski fakultet - Institut za pedagogiju i andragogiju, Beograd
dc.rightsopenAccess
dc.sourceAndragoške studije
dc.subjectupravljanje učenjemsr
dc.subjectotkrivanje zakonitosti u podacimasr
dc.subjectmodelovanje društvenog okruženjasr
dc.subjectanalitika u učenjusr
dc.subjectmodelling the social environmenten
dc.subjectlearning managementen
dc.subjectlearning analyticsen
dc.subjectdata miningen
dc.titleMogućnosti pristupa zasnovanog na podacima u upravljanju procesom učenjasr
dc.titleA data-driven approach to learning management: Possibilities, challenges, and limitsen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage155
dc.citation.issue1
dc.citation.other(1): 135-155
dc.citation.rankM24
dc.citation.spage135
dc.identifier.doi10.5937/AndStud1901135P
dc.identifier.fulltexthttp://prototype2.rcub.bg.ac.rs/bitstream/id/597/1972.pdf
dc.identifier.rcubconv_290
dc.type.versionpublishedVersion
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairetypearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Radovi istraživača / Researchers’ publications
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