Abstract-Students' motivation is an important factor in ensuring the success of e-learning implementation. In order to ensure students is motivated to use e-learning, motivational design has been used during the development process of e-learning applications. The use of gamification in learning context can help to increase student motivation. The ARCS+G model of motivational design is used as a guide for the gamification of learning. This study focuses on the influence of gamification on students' motivation in using elearning applications based on the ARCS+G model. Data from the Instructional Materials Motivation Scale (IMMS) questionnaire, were gathered and analyzed for comparison of two groups (one control and one experimental) in attention, relevance, confidence, and satisfaction categories. Based on the result of analysis, students from the experimental group are more motivated to use e-learning applications compared with the controlled group. This proves that gamification affect students' motivation when used in elearning applications.Index Terms-Gamification, Motivational Design, ARCS Model, ARCS+G Model.

Most e-Learning web application known as Learning Management Systems are associated with collaboration in a web page. It allows a user to interact directly with multiple application in any web platform together with other users. However, the action of the users has not been thoroughly analyzed. Due to the medium of teaching, implementation is through online. It is nec-essary to analyse each student behaviour characteristics of blended learning implementation so that lecturer can adjust how online activities are per-formed. In this paper, we propose a conceptual model in profiling student behaviour in e-Learning based on metadata approach and Community of In-quiry Model. We adopt a metadata approach in collecting student experience in e-Learning and Community of Inquiry Model to mapping the online stu-dent experiences. This conceptual model provides the basis for evaluating student behaviour characteristics in online learning with the goal of im-proved student engagement and online activity design.

ARCS model is a motivational design model for learning environments to stimulate and sustain learner motivation using the problem-solving approach. The ARCS model often used to identify and solve problems in a systematic way to motivate a learner in classroom learning or e-learning environment. The gamification of learning provides a useful technique to drive engagement and motivation in applying game mechanics and dynamics into learning activities. In spite of the success of gamification of learning, there are also failures. The proper method is needed to integrate gamification into learning. In this paper, we propose an enhanced ARCS model for gamification of learning called ARCS+G. The ARCS+G model is aimed to provide a solution for the problems in utilizing gamification of learning.

Electronic Learning (E-Learning) evolves consistently. An effective eLearning has unrelenting readiness in integrating new paradigms while the students become the core of the process. In this regard, the current tools are replaced by others, with customization under consideration. Somehow, changes are costly and should not be merely focusing on replacing all past initiatives but also should attempt to combine the new initiatives with the successful ones, in order that the great robustness and effectiveness of learning environments could be assured. Accordingly, the presently available integration initiatives will be reviewed in this paper. A new initiative will be proposed as well.

Advances in technology have allowed mobile devices to be used for learning purpose. The use of tablets in mobile learning has the potential to enhance learning, contributing to increased motivation and knowledge acquisition. However, without careful planning and support for learning content, students may not have the behavioural intention to use tablets in their learning. Previous studies have focused on the use of learning applications installed in the tablets. There is a lack of research on students' behavioural intentions to use tablets in learning. The partial least squares regression approach and the Unified Theory of Acceptance and Use of Technology (UTAUT) were used in this study to explore students’ behavioural intention to use the tablet in learning. This study was conducted in a private school in Malaysia. A total of 170 participants were enrolled in this study. The results showed that most of the hypotheses of the study were not supported and further revealed that the construct of performance expectancy was the only determinant of students’ behavioural intentions to use the tablet in learning.

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Multi-label classification is a technique used for mapping data from single labels to multiple labels. These multiple labels stand part of the same label set comprising inconsistent labels. The objective of multi-label classification is to create a classification model for previously unidentified samples. The accuracy of multi-label classification based on machine learning algorithms has been a particular study and discussion topic for researchers. This research aims to present a systematic literature review on multi-label classification based on machine learning algorithms. This study also discusses machine learning algorithm techniques and methods for multi-label classification. The findings would help researchers to explore and find the best accuracy of multi-label classification. The review result considered the Support Vector Machine (SVM) as the most accurate and appropriate machine learning algorithm in multi-label classification.

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