Open AccessUser behaviour pattern for online learning system: UiTM iLearn portal case
Author(s) -
Siti Fairuz Nurr Sadikan,
Azizul Azhar Ramli,
Mohd Farhan Md Fudzee,
Siti Sapura Jailani,
Mohd Ali Mohd Isa,
Prasanna Ramakrisnan,
Roslani Embi
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v15.i1.pp382-390
Subject(s) - web log analysis software , computer science , world wide web , context (archaeology) , cluster analysis , web server , multimedia , information retrieval , the internet , artificial intelligence , static web page , paleontology , biology
A Web server log files contain an entire record of the user’s browsing history such as referrer, date and time access, path, operating system (OS), browser and IP address. User navigation pattern discovery involves learning of user’s browsing behaviour to gain the pattern from web server log file. This paper emphasizes on identifying user navigation pattern from web server log file data of iLearn portal. The study implements the framework for user navigation including phases of acquisition of weblog, log query parser, preprocessor, navigational pattern modelling, clustering, and classification. This study is conducted in the context of the actual data logs of the iLearn portal of Universiti Teknologi MARA (UiTM). This study revealed the navigational patterns of online learners which relatively related to their intake or group along the semester of 14 weeks. Besides, access patterns for students along the semester are different and can be classified into three (3) quarter, namely Q1, Q2 and Q3 based on the total of week per semester. Future work will focus on the development of prototype to improve the security of online learning especially during the assessment progress such as online quiz, test and examination.