Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Using Multivariate Statistics

Evaluating E-learning systems success : an empirical study

Extension of the Technology Acceptance Model: Four Longitudinal Field : .

Massive open online courses (MOOCs) are recent innovative approaches in distance education, which provide learning content to participants without age‐, gender‐, race‐, or geography‐related barriers. The purpose of our research is to present an efficient sentiment classification scheme with high predictive performance in MOOC reviews, by pursuing the paradigms of ensemble learning and deep learning. In this contribution, we seek to answer several research questions on sentiment analysis on educational data. First, the predictive performance of conventional supervised learning methods, ensemble learning methods and deep learning methods has been evaluated. Besides, the efficiency of text representation schemes and word‐embedding schemes has been evaluated for sentiment analysis on MOOC evaluations. For the evaluation task, we have analyzed a corpus containing 66,000 MOOC reviews, with the use of machine learning, ensemble learning, and deep learning methods. The empirical analysis indicate that deep learning‐based architectures outperform ensemble learning methods and supervised learning methods for the task of sentiment analysis on educational data mining. For all the compared configurations, the highest predictive performance has been achieved by long short‐term memory networks in conjunction with GloVe word‐embedding scheme‐based representation, with a classification accuracy of 95.80%.

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

The role of transformational leadership as a mediating variable in DeLone and McLean information system success model: The context of online learning usage in Yemen

Educational data mining and learning analytics for 21st century higher education: A review and synthesis

In the coming years, technology will impact the learning experience in many ways. Internet of Things (IoT) continues to confirm its important position in the context of Information and Communication Technologies and the development of society. With the support of IoT, institutions can enhance learning outcomes by providing more affluent learning experiences, improved operational efficiency, and by gaining real-time, actionable insight into student performance. The purpose of this study is to find out the potential of IoT in higher education and how to maximize its benefits and reducing the risks involved with it. Further efforts are necessary for releasing the full potential of IoT systems and technologies. Therefore, this paper presents a study about the impact of IoT on higher education especially universities. IoT stands to change dramatically the way universities work, and enhance student learning in many disciplines and at any level. It has huge potential for universities or any other educational institutions; if well prepared to ensure widespread and successful implementation by leadership, staff, and students. IoT needs development where universities can lead. Academics, researchers, and students are in a unique place to lead the discovery and development of IoT systems, devices, applications, and services. Moreover, this paper provides an evidences about the future of IoT in the higher education during the next few years, which have offered by a number of research organizations and enterprises. On the other hand, IoT also brings tremendous challenges to higher education. Hence, this paper also presents the perspective on the challenges of IoT in higher education.

https://iopscience.iop.org/article/10.1088/1742-6596/892/1/012017/pdf

Internet of Things in Higher Education: A Study on Future Learning

Massive open online courses (MOOCs) are among the latest e-learning initiative that have gained a wide popularity among many universities. Student dropout in MOOCs is a major concern in the higher education and policy-making communities. Most student dropout is caused by factors outside the institution’s control. In this study, a multiple-criteria decision-making method was used to identify the core factors and possible causal relationships responsible for the high dropout rate in MOOCs. Twelve factors, distributed across four dimensions, related to students’ dropout from online courses were identified from the literature. Then, a total of 17 experienced instructors in MOOCs from different higher education institutions were invited to assess the level of influence of these factors on each other. The results identified six core factors that directly influenced student dropout in MOOCs, these were: academic skills and abilities, prior experience, course design, feedback, social presence, and social support. Other factors such as interaction, course difficulty and time, commitment, motivation, and family/work circumstances were found to play a secondary role in relation to student dropout in MOOCs. The causal relationships between the primary and secondary factors were mapped and described. Outcomes from this study can offer the necessary insights for educators and decision makers to understand the cause–effect relationships between the factors influencing MOOC student dropout, thus providing relevant interventions in order to reduce the high dropout rate.

Factors affecting student dropout in MOOCs: a cause and effect decision‐making model

Blended learning has been growing in demand and popularity and has become a common phenomenon in modern higher education systems, especially with the implementation of learning management system (LMS). This paper examined the effects of critical success factors on students’ experience and satisfaction with the LMS in a blended learning setting. Certain characteristics of students, instructors, system, classmates, course design, and organization were identified from the literature as the main indicators of the LMS success. A total of 174 university students (94 males and 80 females) responded to an online questionnaire. A partial least square (PLS) approach was used for data analysis. The result showed that certain factors were found to influence students’ perceived ease of use and usefulness of LMS and consequently their satisfaction. Findings from this paper provide the necessary insights as to how universities can enhance students’ experiences and satisfaction of LMS in order to support the blended learning approach.

“I am Still Learning”: Modeling LMS Critical Success Factors for Promoting Students’ Experience and Satisfaction in a Blended Learning Environment

Yemen is confronted with several challenges in implementing e-learning in public universities. Thus, this research paper aims to identify and addressed the major issues and challenges faced by a public university in Yemen in establishing e-learning as a successful medium of imparting learning process. These challenges and barriers are discussed in detail. Many issues of e-learning relevant to the context of Yemen are also identified based on literature review. This paper also discusses the enabling factors and benefits for using e-learning. Two separate research methods are implemented: a survey and an interview to study the respondent’s individual learning experience and what are the challenges and problems of using e-learning from their point of view. This study was conducted at Hodeidah University. Two major groups of informants are used and they are academic staff and administrators in the university. Findings show that barriers such as lack of quality e-content, lack of awareness, lack of skills, lack of foreign language skill, attitudinal hampering, infrastructural obstacles, cultural barriers and high rate of computer illiteracy in addition to barriers related to integrating e- learning into traditional education methods are the major challenges of implementing e-learning. The results show that all the obstacles can be categorized into five dimensions which are a human constraints, administrative constraints, technical constraints, financial and physical constraints. The results also revealed that factors such as technical suppor, social and cultural support of e-learning, financial support for instructors and faculty members, improving working conditions, improving technological background knowledge, improving foreign language skill, providing royalties on copyrighted materials and academic staff attention in professional development are the important issues in implementing e-learning in any public university in Yemen.

Hanan Aldowah

Papers

Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Internet of Things in Higher Education: A Study on Future Learning

Factors affecting student dropout in MOOCs: a cause and effect decision‐making model

“I am Still Learning”: Modeling LMS Critical Success Factors for Promoting Students’ Experience and Satisfaction in a Blended Learning Environment

Issues and Challenges of using E-Learning in a Yemeni Public University