This study presents three frameworks for mobile learning system based on quality factors derived from the updated DeLone and McLean information system success model. This study used the questionnaire as a quantitative method to explore quality factors for mobile learning system based on perspectives of 392 students. This study opens future work for using the identified quality factors as guidelines for researchers and designers to design and develop mobile learning applications

M-learning is a form of learning, which has similarities and differences with the electronic learning (e-learning). It is mainly based on the use of the mobile wireless technologies that allow for learners to easily access learning materials anytime he desires and anywhere, whether on campus or off campus. Therefore, this creates a new flexible learning environment in the context of different learning settings. Students' perception of such technology is one of the most important factors for successful adoption of m-learning in the higher education environment. This study is conducted to investigate the perceptions of students in University Malaysia Terengganu (UMT) to move towards applying m-learning in their studies by using their mobile devices and to explore their expectations on mobile learning services. A total number of 91 undergraduate students majoring in computer science participated in the study. The findings show that the students have positive perception towards mobile learning and would like to use their mobile devices for both learning and administrative services.

Stemming is one of the pipeline feature of Information Retrieval and commonly used in natural language processing and text mining. The main purpose of a stemming process is to reduce the inflectional or derivational word into its root form. The difficulties on developing stemming algorithm is to identify and remove affixes since each language in the world has unique characteristics and grammatical rules. This paper compares related study on existing stemmers to be used in Uzbek language. We discuss the type of stemming algorithms, an overview of available popular English stemmers and comparison between discussed stemmers as well as their evaluation and analysis of available stemmers on Uzbek language experiment. Based on the comparative study and experiment, we proposal our model of the Uzbek stemmer that enhances some of the features in Lovins stemmer to suit the requirements for the Uzbek language.

Learning style (LS) is a description of the attitudes and behaviors which determine an individual’s preferred way of learning. Since each student has different LS, it is important for the teacher to recognize the differences in LS. Thus, an appropriate technique to detect students' LS, improve the motivation and academic achievement are required. The common approach using questionnaires to identify LS is less accurate due to complete the questionnaire is a tedious task for students and tend to choose answers randomly without understanding the questions. Emotions such as anger, sadness, and happiness resulting the different questionnaire answers. Due to the approach constrains, this study has focused on automated approaches that identify student LS from student behavior in the learning process. Implementation of decision support system (DSS) as automated application systems is needed to help teachers make decisions in determining students' LS. Thus, the objective of this study is to propose the architecture of LS detection automatically using decision support system. The development of the architecture is applying the behavioral modelling, that are contained student’s behavior parameters for visual-auditory-kinesthetic (VAK) model. Evaluation of the architecture is tested with the precision DSS engine. The accuracy of the rule technique achieves significant 80% accuracy. This study aims to help teachers to identify the ability of the student through the learning style (LS) in order to create effectiveness of learning and improving student’s achievement indirectly. Keywords— decision support system, reasoning engines, learning style detection, user behavior, visual-auditory-kinesthetic (VAK) model