DocumentCode :
3589985
Title :
Analysis of students behaviour in virtual environment
Author :
Reichel, J. ; Kuna, P.
Author_Institution :
Dept. of Inf., Constantine the Philosopher Univ., Nitra, Slovakia
fYear :
2014
Firstpage :
419
Lastpage :
423
Abstract :
The analysis of students behaviour in web learning environment within distance learning is one of the most significant areas for learning optimization. The aim of this article is to analyse students behaviour and the use of e-learning course in subject Discrete Mathematics. Data and results of this analysis are important for further adjustment and improvement of the e-course. Results of the course traffic analysis were estimated using association rules Discrete mathematics is compulsory for both bachelor and master study program Applied Informatics in full-time and distance form of study as well as bachelor and master study program Teaching of academic subjects in full-time and distance form of study. This electronic course is designed to use linear and branched teaching programs. In compiling the course we tried to take into consideration target audience - students of Computer Science. The course is designed to not require any special knowledge in IT field. Discrete Mathematics Course 1 consists of 10 thematic units (areas) replicating the length of the semester in weeks. Authors describe detail analysis of students behaviour which is made of data taken from LMS MOODLE database. We used specific types of data, which are indicating user traffic on every single page of the course. We used a log file that contains records of e-learning course with 107 students. To identify sessions, we used the STT (Session Timeout Threshold). Purpose of session identification is to divide access of all users into separate sessions (relations). Session side-effect may exclude users who are behind a NAT or proxy device. So we can identify users who are sharing a single computer, for example in a library. Students who used the e-course of Discrete Mathematics 1 were more successful in the final examination. The fact that the course is effective does not mean that all activities have been fully utilized. Based on the results of our analysis, we can optimize and improve the e-course and - ring it closer to the student´s needs. After implementation of necessary changes we can evaluate impact of these changes in the efficacy of the course.
Keywords :
Internet; behavioural sciences computing; data mining; distance learning; educational courses; learning management systems; mathematics computing; teaching; Applied Informatics master study program; Computer Science; LMS MOODLE database; NAT device; Web learning environment; association rules; branched teaching programs; course traffic analysis estimation; discrete Mathematics subject; distance learning; e-course improvement; e-learning course; electronic course; learning optimization; linear teaching programs; proxy device; students behaviour analysis; virtual environment; Aggregates; Computer science; Data mining; Electronic learning; Informatics; Mathematics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging eLearning Technologies and Applications (ICETA), 2014 IEEE 12th International Conference on
Print_ISBN :
978-1-4799-7739-0
Type :
conf
DOI :
10.1109/ICETA.2014.7107621
Filename :
7107621
Link To Document :
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