Background: Learning Management Systems (LMS) represent one of the main technology to support learning in HE institutions. However, every educational institution differs in its experience with the usage of these systems. South East European University’s LMS experience is longer than a decade. From last year SEE – University is adopting Google Classroom (GC) as an LMS solution. Objectives: Identifying factors which encourage LMS activities, with special emphasis on SEEU, might be of crucial importance for Higher Education academic leaders as well as software developers who design tools related to fostering LMS. Methods/Approach: This paper introduces new approach of investigating the usage of LMS, i.e. identifying the determinants of increasing usage of LMS activities, by conducting empirical analysis for the case of SEEU. We apply appropriate estimation technique such as OLS methodology. Results: Using SEEU Usage Google Classroom Report & Analysis Data for spring semester (2016–2017...

https://www.ijert.org/the-relation-between-big-data-technology-and-e-learning https://www.ijert.org/research/the-relation-between-big-data-technology-and-e-learning-IJERTV3IS041525.pdf Big Data has been an emerging topic especially when it is considerably linked with E-learning. In today's era, most of the people are interested in fetching major part of the information like to buy or sell (i.e. e-commerce transactions), are all performed through cloud which is the other name of internet. People are learning and even developing new sources of information and income through e-learning. But the problem is that web has a humongous amount of structured and even un-structured data which can be given a systematic presentation in E-learning and users may get maximum information through interfaces. This signifies the reason that this literature may boost the upcoming learning techniques and technologies when we combine the benefits of Big Data to increase velocity and volume of E-learning and all its types. E-learning needs different sources to extract insights. And through this study help can be taken that how, the complex nature of Big Data can be taken a benefit of with using it in all different modes of E-learning and can promote the usage though many institutes are unaware of the usage of Big Data. A further research can also be conducted in which learning analytics can be extracted with the help of Big Data information in this study and new research in Artificial Intelligence and easy learning can be promoted through this study.

Electronic learning (e-learning) plays a significant role in improving the efficiency of the education process. However, in many cases in developing countries, technology transfer without consideration of technology acceptance factors has limited the impact of e-learning and the expected outcome of the education process. Therefore, this shift in learning method has been met with low enthusiasm from academic staff and students owing to its low perceived usefulness and perceived ease-of-use. The University of Kufa (UoK) in Iraq is considered a good case study because it has implemented the e-learning platform since 2013. The UoK platform is based on open-source Moodle owing to the latter's advantages, such as low implementation cost, open community for support and continuous update and development. To identify and evaluate the challenges, this study uses a questionnaire survey that targets the level of adoption, implementation, familiarity and technology acceptance of staff and students. A total of 242 educators participate in the survey, and the data are subsequently analysed. Important information is extracted using data mining techniques, namely clustering and decision trees. One of the main crucial factors extracted from the analysis results is the perception that social media is easier to use compared with a dedicated e-learning platform such as Moodle. This factor may also discourage educators/learners from adopting an offered e-learning platform, regardless of actual usefulness, motivation and training programs. Therefore, this paper offers practical information regarding the main issues and a guideline to fully utilise e-learning for policy makers and e-learning developers, particularly in newly established institutions or developing countries.

Analytics as one of the recent fields in technology-based learning offers many benefits to educators, instructors, and administrators to improve the efficiency and quality of alternative educational materials, and learning experience through tracking and storing students' log data on web platforms over an extended period of time. This mixed-method study investigates students' log data retrieved from the opencourseware (OCW) specifically launched for a required academic English speaking skills course offered at Middle East Technical University in Turkey with the aim of enhancing the quality and efficiency of the materials available for the course. By understanding the reasons behind students' behaviors via the interviews conducted with 50 students on this online courseware, this study also aims to provide useful practical hints to the instructors and guide them to act on future decisions. The analyzed data is based on learner behavior with a specific emphasis on average view duration, likes and dislikes, and comments. This study can serve as a starting point to guide and provide the instructors and administrators about the future of the aforementioned course which is also offered in a rotational hybrid learning format where the effectiveness of online materials gain even more importance.

Analytics as one of the recent fields in technology-based learning offers many benefits to educators, instructors, and administrators to improve the efficiency and quality of alternative educational materials, and learning experience through tracking and storing students’ log data on web platforms over an extended period of time. This mixed-method study investigates students’ log data retrieved from the opencourseware (OCW) specifically launched for a required academic English speaking skills course offered at Middle East Technical University in Turkey with the aim of enhancing the quality and efficiency of the materials available for the course. By understanding the reasons behind students’ behaviors via the interviews conducted with 50 students on this online courseware, this study also aims to provide useful practical hints to the instructors and guide them to act on future decisions. The analyzed data is based on learner behavior with a specific emphasis on average view duration...

Evolution in managing information systems and the big datasets that increasing every minute, generates the vital demand competition to explore the maximum benefits of the „big data‟ creating in higher education institutes . Business intelligence (BI) and learning analytics (LA) are nowadays draw attention in business and society and being one of the most competitive advantages themes and profitable areas of interest in industry and organizations, including higher education institutions. Business intelligence Predictive analytics systems, using BI tools have established substantial effect on tactical transformation, decision support and informing trends‟ predicting. Learning analytics is working to deploy the worth of business intelligence in the academic context arena of education and training as it influences students‟ success, retention and explore enhancement to improve satisfaction. These concepts extent the process from envisioning the problem to applying learning analytics techniques to a particular situation, achieving insight to help deploy the results to improve decision-making. This paper is based on a real dataset in a case study grounded in “Computer Science and Information Technology” college in Sudan university of science and technology (SUST), and focuses on investigating the potential of applying Business Intelligence approaches toward an expressive analysis of the organization‟s and student‟s experience, by making use of “business intelligence enhanced learning analytics framework”, showing challenges and opportunities. It provides valuable insights into build a profound knowledge about students‟ experience, so as to assess the situation in teaching- learning process, by identifying weaknesses to be considered through practical institutional responses and, prepares for smart informative supervision. The findings explored that, how technology captured data of students‟ performance for prediction, to identify at risk students, for the purpose of consulting and withholding them before being drop out, investigate the states and provinces that have very small number of students, the effect of number of students per college/department on average GPA, the individual student details are showed, such as his personal profile and his results in his complete educational life and the completion rate for graduated students is calculated. Moreover, how that constructions can take the form of statistical outlines, models, KPIs, insightful and interactive dashboards and relationships, offering a grand challenge for technology enhance learning (TEL).