Metody pro výpočet podobnosti výukových položek – Bc. Dominik Gmiterko
Bc. Dominik Gmiterko
Bachelor's thesis
Metody pro výpočet podobnosti výukových položek
Techniques for measuring similarity of educational items
Abstract:
Táto práca sa venuje metódam pre výpočet podobnosti výukových položiek na základe správnosti odpovedi od používateľov. Už pred začiatkom práce sme si boli vedomý, že pri takomto určení podobnosti sa môžu vyskytnúť neobjasnené pravidelnosti. Vo výukovom systéme Umíme česky sa prejavili ako rozdelenie položiek podľa ich správnej odpovede a levelu v ktorom sú zaradené. Jadro práce tvorí exploratívna analýza …moreAbstract:
This work focuses on the measuring of item similarity in tutoring systems utilizing correctness of answers from users. We knew that some unexplained regularities might appear in a similarity of items. In the system Umíme česky they caused separation of items based on their correct answer and level they are assigned into. The core of the work consists of an explorative analysis of possible causes for …more
Language used: English
Date on which the thesis was submitted / produced: 24. 5. 2018
Identifier:
https://is.muni.cz/th/l5dzj/
Thesis defence
- Date of defence: 28. 6. 2018
- Supervisor: doc. Mgr. Radek Pelánek, Ph.D.
- Reader: doc. Mgr. Jan Obdržálek, PhD.
Citation record
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- světu
Other ways of accessing the text
Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatikyMasaryk University
Faculty of InformaticsBachelor programme / field:
Informatics / Computer Systems and Data Processing
Theses on a related topic
-
Adaptive Learning of Programming
Tomáš Effenberger -
Adaptive Learning of Programming
Tomáš Effenberger -
Effective Learning Environment for Cybersecurity Hands-on Training
Pavel Šeda -
Extension of clustering analysis tool for cybersecurity exercises
Tomáš Ondruško -
Explaining convolutional neural network using clustering methods
Adam Bajger -
Customers Classification using Recency Frequency, Monetary value (RFM), and K-means clustering algorithm
Mark Azietaku -
Evaluation of Multiple Clustering Algorithms on Insurance Datasets
Kryštof Suchánek -
Comparison of methods for clustering convolutional neural network intercomputation values with respect to explainability
Adrián Bindas