The digitization of nearly all media and the increasing migration of social and economic activities to the İnternet, the development of social networking technologies, the İnternet of Things and cloud computing caused rapid increase in the volume of data and the formation of Big Data paradigm. Big Data involves technologies and tools for collecting, processing, analyzing and extracting useful knowledge from structured and unstructured data of large volumes generated at high speed by different sources. Increasing the volume, speed, diversity and value of Big Data began to play an important role in the creation of social relationships, competitive advantage and innovative fields. The development of the information society, the formation of digital economy, and the application Big Data technologies in different spheres of human activity required the quantitative and qualitative assessment of Big Data. In this article some approaches relate to the definition of Big Data have been reviewed. Methodological approaches and indicators for measuring Big Data have been researched. At the end, the indicators have been proposed for the measurement of factors that affected the growth and development of Big Data.

This paper intends to develop an evaluation framework for the students with disabilities based on speech recognition technology. Education is the most significant ingredient in the development and empowerment of individuals. Till the last decade, education was provided to the persons with disabilities in segregated school settings or “special schools”. But in the recent years, there has been a great shift in societal attitude towards disabled students globally. The calls for “integration” of all students, disabled students and non–disabled students into the mainstream classroom environments have gathered momentum worldwide. In the pre–existing frameworks, the disabled students faced great difficulty while interacting with the system. The prime objective of our proposed framework is to provide a user–friendly and interactive environment that gives equal opportunities to all the students being evaluated. The utilization of speech recognition technology would lead to the elimination of all misinterpretations arising due to the human scribe or mediator and would enhance the ability of the disabled students to keep pace with the other students.

Intelligent agents have been used in collaborative learning. However, they are rarely used to facilitate group interactions in collaborative m-learning environments. In view of this, the paper discusses the use of intelligent agents in facilitating collaborative learning in mobile learning environments. The paper demonstrates how to design intelligent agents and integrate them in collaborative mobile learning environments to allow group learners to improve their levels of group knowledge construction. The design was implemented in a collaborative mobile learning system running on Modular Object-Oriented Dynamic Learning Environment (Moodle) platform. The application was used in some experiments to investigate the effects of those facilitated interactions on the level of group knowledge construction. The results showed improved levels of group knowledge construction in instances where the facilitations were enabled compared to where they were disabled. The paper concludes that the use of intelligent agents in facilitating learner group interactions in collaborative mobile learning environments improves the levels of group knowledge construction. For future work, the use of intelligent agents can be tested in other areas of group interactions to enhance group learning.

Social media and micro-blogging websites have become the popular platforms where anyone can express his/her thoughts about any particular news, event or product etc. The problem of analyzing this massive amount of user-generated data is one of the hot topics today. The term sentiment analysis includes the classification of a particular text as positive, negative or neutral, is known as polarity detection. Support Vector Machine (SVM) is one of the widely used machine learning algorithms for sentiment analysis. In this research, we have proposed a Sentiment Analysis Framework and by using this framework, analyzed the performance of SVM for textual polarity detection. We have used three datasets for experiment, two from twitter and one from IMDB reviews. For performance evaluation of SVM, we have used three different ratios of training data and test data, 70:30, 50:50 and 30:70. Performance is measured in terms of precision, recall and f-measure for each dataset.

The objective of this article is to make a bibliographic study on the recommendation of learning activities that can integrate user mobility. This type of recommendation makes it possible to exploit the history of previous visits in order to offer adaptive learning according to the instantaneous position of the learner and the pedagogy of the guide. To achieve this objective, we review the existing literature on the recommendation systems that integrate contexts such as geographic location and training profile. Next, we are interested in the social relationships that users can have between themselves. Finally, we focus on the work of recommending mobile learning activities in the context of scenarios of field trips.

Electronic voting is a voting process using electronic mean that allows voters to cast their secret and secure vote over an unsecured channel. Many forward-thinking countries are adopting the electronic voting system to upgrade their election process. Since E-voting system is more complex so it requires more security as compared to the postal voting system. One of the fine tool to provide the voter anonymity is the blind signature scheme. Many blind signature proposals based on traditional public key cryptosystem have been discussed, however, they get the worst of certificate and public key management. In this sense, the objective of the paper is twofold. Firstly, we proposed a blind signature scheme using the identity-based cryptosystem. Proposed scheme uses the combination of Bolyreva’s blind signature scheme and Cha-Chaon’s Identity-based signature. Secondly, we show that proposed scheme is more suitable for E-voting system as compared with others ID-based blind signature scheme.

The aim of this paper is to explore the characteristics of the learners used by the developed adaptive educational hypermedia systems to date to draw conclusions about their relation to the adaptation techniques they use and to be explained the rationale for selecting of the learners' characteristics used by the adaptive educational hypermedia system MATHEMA for its adaptation techniques. At first, the characteristics of learners using some systems for their adaptive techniques are presented. Here is the presentation of learner’s characteristics used by adaptation techniques of the adaptive educational hypermedia system MATHEMA. Finally, an evaluation of the main functions of the MATHEMA is performed. In conclusion, we discuss the choice of specific learners' characteristics for adaptation techniques of the MATHEMA and the resulting educational benefits.