Detecting disengagement of online students through log files analysis

@inproceedings{10868,
   author = {Lustigová, Zdena and Brom, Pavel and Novotná, Veronika},
   address = {Fiorenze},
   keywords = {disangagement; online students; log files analysis; data mining},
   language = {eng},
   location = {Fiorenze},
   publisher = {Pixel Associazione Culturale},
   title = {Detecting disengagement of online students through log files analysis},
   year = {2014}
}

TY  - JOUR
ID  - 10868
AU  - Lustigová, Zdena - Brom, Pavel - Novotná, Veronika
PY  - 2014
TI  - Detecting disengagement of online students through log files analysis
PB  - Pixel Associazione Culturale
CY  - Fiorenze
KW  - disangagement
KW  - online students
KW  - log files analysis
KW  - data mining
N2  - To achieve effective learning, motivational aspects like engagement play a very important role. Within online learning applications the disengagement detection and prediction based on real data (not always in real time) is becoming more and more popular among educational specialists. Many E-learning systems, and virtual or remote learning environments, could be improved by tracking students’ disengagement that, in turn, would allow personalized interventions at appropriate times in order to re-engage students. The present article describes the results of a medium-scale (N = 56) study, using log files from Open Remote Laboratory at Charles University in Prague, Faculty of Mathematics and Physics, to observe secondary school students’ behaviour during their work in virtual environment. Simple data mining and text mining techniques were used to reveal individual user’s behavioural patterns and to detect disengagement. The results will be used mainly to improve the systems’ adaptability to students’ requirements and to prevent their disengagement.
ER  - 

LUSTIGOVÁ, Zdena, Pavel BROM a Veronika NOVOTNÁ. \textit{Detecting disengagement of online students through log files analysis}. Fiorenze: Pixel Associazione Culturale, 2014, 6 s.