Automated extraction and visualization of learning concept dependencies using Q-matrices and exam results

Information systems of educational organizations often represent a potential well of useful information which can be discovered and interpreted by using specific methods. Exam results in particular are commonly used as a single-use measure of individual knowledge states, after which they are archive...

Full description

Saved in:
Bibliographic Details
Published in2016 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM) pp. 1 - 5
Main Authors Vranic, Mihaela, Pintar, Damir, Humski, Luka
Format Conference Proceeding
LanguageEnglish
Published University of Split, FESB 01.09.2016
Subjects
Algorithm design and analysis
Annotated exams
Association rule
Concept maps
Data mining
Data visualization
Education
Education improvement
Electrical engineering
Frequent itemsets
Itemsets
Learning concepts
Q-matrix
Transactional data
Tree-like structures
Visualization
Online AccessGet full text

Cover

More Information
Summary:Information systems of educational organizations often represent a potential well of useful information which can be discovered and interpreted by using specific methods. Exam results in particular are commonly used as a single-use measure of individual knowledge states, after which they are archived and subsequently never used again. Our approach suggests using past exam results as a rich data source for extracting knowledge about learning concepts, especially regarding their mutual relationships. To achieve this goal, we adopt our method for interactive visualization of patterns in transactional data and apply it to knowledge state matrices generated from real-life exam results and Q-matrices constructed by domain experts, providing the end user with rich, easily interpretable and visually engaging dendrogram structures.
ISSN:1847-358X
DOI:10.1109/SOFTCOM.2016.7772122