This HTML5 document contains 49 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n11http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n19http://purl.org/net/nknouf/ns/bibtex#
n13http://localhost/temp/predkladatel/
n21http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n6http://linked.opendata.cz/resource/domain/vavai/projekt/
n15http://linked.opendata.cz/ontology/domain/vavai/
n8https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n12http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F61988987%3A17310%2F07%3AA1000KU9%21RIV10-MSM-17310___/
n9http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n10http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n16http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n4http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n20http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n17http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F61988987%3A17310%2F07%3AA1000KU9%21RIV10-MSM-17310___
rdf:type
n15:Vysledek skos:Concept
dcterms:description
Mining knowledge from logs of adaptive system is one of methods, how to obtain information, which can be used to verify used adaptation scheme and to determinate possible modifications of learning materials. This paper is focusing on statistic methods, which can show differences between several groups of users and determine knowledge cohesion of users in progress and at the end of the course. To determine difficult or simple questions in tests level of relevancy can be used. For log processing can be used methods of data analysis, such as clustering or decision trees. These methods are useful for detection of navigation in course and for determination, which parts of offered learning material is necessary to know for particular final mark. Mining knowledge from logs of adaptive system is one of methods, how to obtain information, which can be used to verify used adaptation scheme and to determinate possible modifications of learning materials. This paper is focusing on statistic methods, which can show differences between several groups of users and determine knowledge cohesion of users in progress and at the end of the course. To determine difficult or simple questions in tests level of relevancy can be used. For log processing can be used methods of data analysis, such as clustering or decision trees. These methods are useful for detection of navigation in course and for determination, which parts of offered learning material is necessary to know for particular final mark. Mining knowledge from logs of adaptive system is one of methods, how to obtain information, which can be used to verify used adaptation scheme and to determinate possible modifications of learning materials. This paper is focusing on statistic methods, which can show differences between several groups of users and determine knowledge cohesion of users in progress and at the end of the course. To determine difficult or simple questions in tests level of relevancy can be used. For log processing can be used methods of data analysis, such as clustering or decision trees. These methods are useful for detection of navigation in course and for determination, which parts of offered learning material is necessary to know for particular final mark.
dcterms:title
Knowledge mining from adaptive course Knowledge mining from adaptive course Knowledge mining from adaptive course
skos:prefLabel
Knowledge mining from adaptive course Knowledge mining from adaptive course Knowledge mining from adaptive course
skos:notation
RIV/61988987:17310/07:A1000KU9!RIV10-MSM-17310___
n3:aktivita
n4:P n4:V
n3:aktivity
P(1M0572), V
n3:dodaniDat
n17:2010
n3:domaciTvurceVysledku
n21:1042335
n3:druhVysledku
n18:D
n3:duvernostUdaju
n10:S
n3:entitaPredkladatele
n12:predkladatel
n3:idSjednocenehoVysledku
429217
n3:idVysledku
RIV/61988987:17310/07:A1000KU9
n3:jazykVysledku
n16:cze
n3:klicovaSlova
e-learning; learning technology; web-based courses; adaptive hypermedia; AHA!; statistical evaluation; data analysis
n3:klicoveSlovo
n9:e-learning n9:statistical%20evaluation n9:data%20analysis n9:AHA%21 n9:learning%20technology n9:web-based%20courses n9:adaptive%20hypermedia
n3:kontrolniKodProRIV
[7231E0CE486B]
n3:mistoKonaniAkce
Stara Lesna, High Tatras, Slovakia
n3:mistoVydani
Košice, Slovensko
n3:nazevZdroje
Conference Proceedings from 5th International Conference on Emerging e-Learning Technologies and Applications - ICETA 2007
n3:obor
n20:JC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n6:1M0572
n3:rokUplatneniVysledku
n17:2007
n3:tvurceVysledku
Velart, Zdeněk Bober, Marek Šaloun, Petr
n3:typAkce
n11:EUR
n3:zahajeniAkce
2007-09-06+02:00
s:numberOfPages
6
n19:hasPublisher
elfa, s.r.o., Kosice
n8:isbn
978-80-8086-061-5
n13:organizacniJednotka
17310