Nowadays, universities and other research- and education-based institutions make extensive use of e-learning platforms. Despite the numerous benefits online educational environments provide for enterprises, there are numerous unresolved issues that make it difficult for instructors and training managers to obtain accurate information about students' learning behavior. On the one hand, a lack of instruments for measuring, assessing, and evaluating learners' performance in educational activities may give results in educators failing to ensure the effectiveness of the learning process. On the other side, a rigid framework in educational materials limits pupils from acquiring knowledge in accordance with their preferred learning method. As a result, it is vital to design technologies that monitor and analyze the learner's engagement with the e-learning environment. Business intelligence (BI) and online analytical processing (OLAP) technologies can be used to monitor and evaluate the behavior and performance of learners in e-learning environments. They can also be used to assess the course's structure and efficacy during the learning process. The purpose of this article is to examine the usage of business intelligence and OLAP tools in e-learning environments and to give a case study of how to implement these technologies in an e-learning system's database. Students spend little time on courseware and prefer collaborative activities such as virtual classrooms and forums over simply studying the learning material.
BI is inextricably tied to knowledge management, which encompasses the subprocesses of knowledge generation, knowledge access, and knowledge usage. Access to knowledge is enabled by proper technology, which also contributes significantly to knowledge management inside an organization. The definition of business intelligence has evolved throughout time. Today, we typically think of business intelligence as a tool that facilitates the selection, display, and analysis of data. BI is a collection of procedures, technologies, and tools that enable the transformation of data into information, information into knowledge, and knowledge into plans for executing efficiency and profitability-enhancing business measures. The principles of business intelligence (BI) encompass the data warehouse, business analytics tools and content, and knowledge management. In e-learning, the objective of business intelligence is to give users with information that will assist them in achieving greater success and efficiency. To organize and implement e-learning successfully, we require a diverse group of stakeholders (learners, instructors, e-learning content authors, e-learning providers, clients). All exercises are designed for individual learners, with the goal of increasing the efficiency of their learning processes and enhancing their skills. To accomplish these goals, the learning environment must ensure the following functionalities: a useable, user-friendly platform for e-learning, high-quality e-content and e-courses, and e-learning support services.
Data Mining
Analysing data from multiple angles and synthesizing it into valuable information that can be utilized to enhance income, decrease expenditures, or both. Data mining is used to mine information from massive data sets. Data mining is the process of detecting patterns or connections in huge datasets and warehouses. Predicting, grouping, mining contacts and patterns and models are all strategies used in educational data mining.
Learning analytics studies intelligent strategies and procedures to improve the learning process for learners, instructors, educational institutions, and clients when training or educating personnel of a firm. The research uses data mining in education and incorporates concepts and techniques from computer science, statistics, psychology, and pedagogy. The goal is to understand the components of learning and so enable all users establish their goals. Learning analytics was first used in 2009 in Horizon Reports [5], a journal about new technologies and research disciplines. According to predictions, in the coming years, learning analytics and other advances and successes in this subject will have a significant impact on learning processes at all educational levels. The purpose of learning analytics is to provide intriguing discoveries to individuals who construct learning circumstances (mentors, authors of e-content, and educational institutions). These findings will help alter learning content, mediate with high-risk learners, and provide information feedback systems regarding learners. Learning analytics methodologies vary. Along with the procedures already mentioned (forecasting, grouping, mining contacts, distilling data for human assessment, and uncovering patterns and models), the following two should be included: (Learner-Learner or Learner-Instructor) Social network analysis is the study of how people interact with each other and how they interact with the subject matter. In addition, students could connect with other students and professors by searching for their names. They reveal what students are doing, how efficient they are, what they enjoy doing, etc. Also, like with data mining in education, it's critical to provide results in a straightforward manner. All methods for assisting students will be developed from clear and understandable presentation. This is because only clear communication of status, pitfalls, and progress can help both the student and the teacher make informed decisions. Monitoring progress, predicting learner success, and identifying possible issues are the key uses of learning analytics A timely intervention with learners who are more likely to drop a subject or an academic year is enabled. Overall, learning analytics analyzes data produced by or connected to learners, searches for ways to track and predict their development, and provides data in a way that allows both instructors and students to intervene and influence the course of further studies.
Analysing Data Visually
Since the human brain can quickly detect patterns and trends in complicated graphic displays, visual data analysis makes advantage of this ability. Presenting data and finding patterns and models in it are two steps of visual data analysis. The first stage is aided by a PC Visualization of data is critical for pattern recognition. It is possible to zoom in and out, highlight specific groupings of data and track their changes over time in the second phase. Everything from text to graphic presentations affects data visualization. Contrary to popular belief, the best-designed visuals are straightforward, direct, and efficient. Monitoring the Learning Process Statistics on the learning process provide the most fundamental information about a learner's current status. This means that data must be given in a straightforward and understandable manner for the learner to track their own progress. To ensure that the facts selected by the system are relevant to the learning process, they must be concise. Participants in e-courses are graded on individual courses, content, or assignments. Performance on knowledge evaluations is evaluated on the most common errors in problem solving on tests. The reports should be informative, clear, and simple. By tracking who logs in, how long they stay on certain pages, and what pages they visit, Pahl and Donnellan give an overview of LMS activity for instructors. So an instructor can rapidly identify the least active students and design more engaging activities to engage them, based on this data. It also shows which pages or activities are popular. Instructors can then analyse why visits are high or low and adjust the learning program accordingly.
Anna Vorontsova
https://www.emerald.com/insight/content/doi/10.1108/JWAM-09-2019-0027/full/html#sec001
O. Moscoso-Zea, J. Castro, J. Paredes-Gualtor and S. Luján-Mora, "A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence & Analytics for Knowledge Management in Education," in IEEE Access, vol. 7, pp. 38778-38788, 2019, doi: 10.1109/ACCESS.2019.2906343.
https://www.ceeol.com/search/article-detail?id=203505
M. H. Falakmasir, S. Moaven, H. Abolhassani and J. Habibi, "Business intelligence in e-learning: (case study on the Iran university of science and technology dataset)," The 2nd International Conference on Software Engineering and Data Mining, 2010, pp. 473-477.
Keywords: Business intelligence, web-learning, online education, social networks.
Business intelligence has become a vital.part for any business to grow in today's world, as majority of companies start using it they will reap profits out of this tech.
ReplyDeleteWhen we look at the developments in the last 20 years of enterprises and parallel information technologies, we can see that one of the most important concepts is data. This importance given to data also increases investments such as time and money on its collection and storage. Today, most institutions have already completed the stages of data collection and storage of this collected data.
ReplyDeleteToday, another issue is occupying the information technology departments of companies. This is the data explosion, or rather, developing a common strategy using the available data and implementing this strategy in various fields.
Thank you for the blog post.
Gizem Incedal