dc.contributor.author |
Benko, Ľubomír
|
cze |
dc.contributor.author |
Reichel, Jaroslav
|
cze |
dc.contributor.author |
Munk, Michel
|
cze |
dc.contributor.author |
Kuna, Peter
|
cze |
dc.date.accessioned |
2017-05-11T10:58:02Z |
|
dc.date.available |
2017-05-11T10:58:02Z |
|
dc.date.issued |
2015 |
eng |
dc.identifier.isbn |
978-1-4673-8534-3 |
eng |
dc.identifier.issn |
|
eng |
dc.identifier.uri |
http://hdl.handle.net/10195/67425 |
|
dc.description.abstract |
One of the most important areas of optimizing the learning environment in distance education is to analyze the behavior of students in eLearning courses. The aim of the paper is to summarize the field of Educational data mining, analyze the behavior of students in e-course Computer data analysis and to present a few cases of a similar analysis of the behavior of students. The results of the analysis may have potential for future use in optimizing the e-course. Analysis results were obtained using extracted association rules from the e-course. This electronic course is designed to use linear and branched teaching programs. Target group research were students of Computer Science, which was reflected in the results. It is not necessary to have special knowledge of IT to work in e-course. The course was created using LMS Moodle, which records the behaviour of users to the database. We used specific types of data, which indicate user traffic on every single page of the course. We used the log file containing records with the behavior of 69 students in e-course. Session identification is for the distribution of accesses of all users of e-course to separated sessions. Students are identified by their login ID. Therefore, we can separate the users who share a computer. Students who have used e-course Computer data analysis, were successful in the final test. By analyzing we can improve e-course. After implementation of necessary changes we can evaluate impact of these changes in the efficacy of the course. |
eng |
dc.format |
p. 1-6 |
eng |
dc.language.iso |
eng |
eng |
dc.publisher |
IEEE (Institute of Electrical and Electronics Engineers) |
eng |
dc.relation.ispartof |
ICETA 2015 - 13th IEEE International Conference on Emerging eLearning Technologies and Applications : Proceedings |
eng |
dc.rights |
Pouze v rámci univerzity |
eng |
dc.subject |
Computer aided instruction |
eng |
dc.subject |
Data handling |
eng |
dc.subject |
Data mining |
eng |
dc.subject |
Počítačem řízené instrukce |
cze |
dc.subject |
Správa dat |
cze |
dc.subject |
Data mining |
cze |
dc.title |
Visit rate analysis of course activities: Case study |
eng |
dc.title.alternative |
Analýza návštěvnosti kurzů: Případová studie |
cze |
dc.type |
ConferenceObject |
eng |
dc.description.abstract-translated |
Jednou z nejdůležitějších oblastí optimalizaci prostředí učení distančního vzdělávání je analyzovat chování studentů v e-learningových kurzích. Cílem článku je shrnout oblasti vzdělávacích dat, analyzovat chování studentů v počítačovém kurzu a prezentovat několik případů podobné analýzy chování studentů. Výsledky analýzy mají potenciál pro budoucí využití při optimalizaci e-learningového kurzu. Výsledky analýzy byly získány pomocí extrahované asociačních pravidel z e kurzu. Tento elektronický kurz je navržen tak, aby použití lineárních a rozvětvených výukových programů. Cílové skupiny pro výzkum byli studenti informatiky, což se odrazilo na výsledcích. Není nutné mít zvláštní znalosti o tom jak pracovat v e-learningovém kurzu. |
cze |
dc.event |
ICETA 2015 - 13th IEEE International Conference on Emerging eLearning Technologies and Applications (26.11.2015 - 27.11.2015) |
eng |
dc.peerreviewed |
yes |
eng |
dc.publicationstatus |
postprint |
eng |
dc.identifier.doi |
10.1109/ICETA.2015.7558507 |
|
dc.relation.publisherversion |
http://ieeexplore.ieee.org/document/7558507/ |
|
dc.identifier.scopus |
2-s2.0-84990960897 |
|
dc.identifier.scopus |
2-s2.0-84990960897 |
|
dc.identifier.obd |
39878725 |
eng |