Classification of learning styles using behavioral features and twin support vector machine

Background and Objective:Internet and computer access have created opportunities for e-learning. Easier access to resources and freedom of action for users is one of the benefits of e-learning. However, e-learning is not as attractive and dynamic as traditional or face-to-face instruction, and in these systems the user's condition, such as learning rate and motivation, is not taken into account. Therefore, the developers of e-learning systems can help to solve the problems mentioned in these systems by considering the learning style and design of interactive user relationships. Automated identification of learning style not only increases the attractiveness of e-learning, but also increases the efficiency and motivation of learners in e-learning environments. Research shows that people differ in decision making, problem solving, and learning. Learning style makes people understand a story differently. For example, people with good visual memory prefer to present topics visually rather than orally. Applying a proper teaching method improves the learner's performance in the learning environment. Lack of attention to students' learning style reduces their motivation and interest in studying and engagement in educational courses. Students’ success is one of the prominent goals in the learning environments. In order to achieve this goal, paying attention to students’ learning style is essential. Being aware of students’ learning style helps to design an appropriate education method which improves student’s performance in the learning environments. In this paper, the aim is to create a model for automatic prediction of learning styles. Methods: Therefore, two real datasets collected from an e-learning environment which consists of 202 electrical and computer engineering students. Behavioral features were extracted from users’ interaction with e-learning system and then learning styles were classified using twin support vector machine. Twin support vector machine is an extension of SVM which aims at generating two non-parallel hyperplanes. This classifier is not sensitive to imbalanced datasets and its training speed is fast. Findings: In this study, increasing the attractiveness of e-learning is emphasized and the issue of automatic recognition of students' learning style has been investigated by MBTI model. Two data sets from the interaction of 202 electrical and computer engineering students with the Moodle e-learning system have been collected. The collected data set is very unbalanced, which has a negative effect on the accuracy of the categories. With this in mind, the twin support vector machine uses the least squares as a binder. The distinctive feature of this category is the low sensitivity to data balance and very high speed. The results show that the proposed method, despite the inconsistency of the data, has performed very well in the classification of students' learning style and accurately recognizes 95% of learning styles.Conclusion: Due to the excellent performance of the proposed method, a new component can be added to e-learning systems such as Moodle by identifying the learning style, content and appropriate teaching method for the learner. Future research could also gather more data from an e-learning environment and categorize learning styles with cognitive characteristics from the learner.   ===================================================================================== COPYRIGHTS  ©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

Internet of Things; a system for improving the higher education system

Background and Objective:Nowadays, information technology, has affected the production, distribution, and use of knowledge-based processes. Among other things, the Internet of Things as a network of objects connected to each other can bring new capacities in all fields. The aim of the present research is to examine the opportunities and challenges that the Internet of Things can have in relation to the higher education system. The concept of connecting devices and objects to each other is a new development on the Internet; anything anywhere can connect to the Internet and be "smart". Connected devices can communicate with each other and share information, then this information can be processed and lead to making decisions. This concept is called the "Internet of Things (IOT)." The wide range of applications of IOT has enabled educational environments at all levels to benefit from it. In terms of the role of IoT in higher education, this domain includes energy storage, monitoring the health and safety of students, optimizing the physical envriornment of the campus and classrooms, as well as enabling students to attend remotely. The main point that seems to differentiate IOT from other past technologies is that older methods cover a limited range of areas of higher education. With the use of various IOT tools, all these tools and facilities can be made intelligent and use for educational, research and service providing purposes. The aim of this study is to investigate the role and function of the IOT in the processes of knowledge production, transmission and application in higher education system. Methods: For this purpose, the systematic approach and the Chelkland SSM method were used. By refer to several internal and external scientific information databases, 25 related articles were identified. Then, without any sampling, all of these articles were studied by the researchers and their contents are divided into four categories: the introduction of the internet of things, the role of the internet of things in educational functions, the opportunities of the internet of things for higher education and the challenges of the internet of things in higher education, and they were organized in the dimensions of the input, process and output of the higher education system. Findings: The findings of the research indicate that the internet of things, by providing advanced information services, provides a flexible and measurable system for academic community that can be used to personalize training and reinforcement of learning, better management of educational processes, and more effective logistic management etc. The use of the internet of things will also challenge higher education, which violation of privacy, security issues, and rising costs are some of these challenges. Conclusion: In the present study, an attempt was made to introduce IOT and its opportunities and challenges for higher education system by reviewing the related literature. IOT is a technology that covers a wide range of applications in the university, from classrooms to laboratories, colleges and parking lots, and more. Within the system, IOT can be used to support the higher education chain and facilitate communication between input, output, and the process, and facilitate monitoring, control, and management of the university's system. In other words, the IOT operates communication center for the university system. In the dimension of higher education and university process, the IOT can be effective in student interaction and participation, evaluation, mental and physical health, classroom management, satisfaction, attendance, time saving as well as faculty management, energy saving, information searching, improving security in the university environment, providing real learning, personal growth and development for both the university instructors and the students, and so on. Of course, as mentioned, the use of this technology has cetain challenges. These challenges include security and privacy risks, high costs, connection to the Internet, scalability, self-organization and acceptance, etce. But despite this, experts generally see the future of this technology as more practical and important than it is now.   ===================================================================================== COPYRIGHTS  ©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================