User behaviour pattern for online learning system: UiTM iLearn portal case

<span>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.</span>

Fast prediction of web user browsing behaviours using most interesting patterns

The prediction of users’ browsing behaviours is essential for putting appropriate information on the web. The browsing behaviours are stored as navigational patterns in web server logs. These weblogs are used to predict the frequently accessed patterns of web users, which can be used to predict user behaviour and to collect business intelligence. However, owing to the exponentially increasing weblog size, existing implementations of frequent-pattern-mining algorithms often take too much time and generate too many redundant patterns. This article introduces the most interesting pattern-based parallel FP-growth (MIP-PFP) algorithm. MIP-PFP is an improved implementation of the parallel FP-growth algorithm and implemented on the Apache Spark platform for extracting frequent patterns from huge weblogs. Experiments were performed on openly available National Aeronautics and Space Administration (NASA) weblog data to test the effectiveness of the MIP-PFP algorithm. The results were compared with existing implementation of PFP algorithms. The results suggest that the MIP-PFP algorithm running on Apache Spark reduced the execution time by a factor of more than 10 times. The effect of sequence length that has been used as input to the MIP-PFP algorithm was also evaluated with different interestingness parameters including support, confidence, lift, leverage, cosine, and conviction. It is observed from experimental results that only sequences of length greater than three produced a very low value of support for these interestingness measures.

Improving Digital Library Interfaces by Investigating the Electronic Activities of Users

The purpose of this study was to examine the navigational patterns and text markings of electronic text readers when they interacted with electronic documents during an active reading process. The readings took place in two settings, private and document sharing, and the results provided us with user-navigational patterns taxonomy.Le but de cette étude était d’examiner les modèles de navigation et de marquage de textes électroniques des lecteurs lorsqu’ils interagissent avec les documents numériques pendant un processus de lecture active. Les séances de lecture ont eu lieu dans deux environnements, à l’aide de documents privés et partagés, et les résultats ont offert une taxinomie sur les modèles de navigation des utilisateurs.