Analytics have historically been used in the business sector to identify trends in consumerism, but learning analytics has become a major technology for the education sector (see Campbell et al, 2007, Colvin et al, 2015, Cooper, 2012a). ). Some researchers view learning analytics as “big data” applied to education. Many reports have indicated that the terminology of learning analytics is named after analytics applied to business (see also Dawson et al, 2014, Dyckhoff, 2013). Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essayLearning analytics is essentially data-driven. The interest in analytical learning is therefore in reality an interest in “Big Data”. Big data refers to the collection of complex and large data sets, which are difficult to process using traditional data processors (see also Dawson et al, 2014). This is information generated from and for educational settings. Over the years, the use of analytics has increased across various industries to solve problems and provide solutions, including determining trends in different businesses. Businesses want insights into purchasing habits in order to realign their marketing campaigns and make them more effective. Some companies want to target individuals' spending habits and know how to manage their inventory levels (Campbell et al, 2007). As an approach, Learning Analytics applies data analysis principles to student learning (Showers 2014). The goal is to offer actionable and accurate insights into the learning process through aggregation, modeling and mining of critical data sources and to provide evidence demonstrating that learning has been improved (Ferguson et al 2017). Learning analytics uses data from various sources for its research. Data can be collected on student attendance, student library usage, student information systems, student participation in online forums and even student biometric information as well as data on the way students interact in a virtual learning environment (see Ferguson 2012). .VLEs or virtual learning environments are important data sources for learning analytics. Popular VLEs or online platforms such as Sakai, Moodle, and Blackboard have become huge data sources for learning analytics. VLEs contain huge resources for tutors and teachers. Through VLEs, students access past exam questions, notes, books and other learning resources. VLEs provide a very important platform and have become the mainstay of education worldwide. VLEs have therefore become important sources of usable data sources for learning analytics researchers. VLEs provide information on how students access resources, which resources they access, how much time students spend on resources, and which resources students use most often. Thus, VLEs provide information about students' learning behavior patterns and indicate how this pattern is developed or can be developed. With the help of learning analytics, teachers gain valuable information and insight into what resources their students are accessing and how they are using them.information and the degree of activity of online students. Students can also gain insight into how engaged they are in their studies compared to their course mates. Learning analytics provides real-time insights. This way, faculty and students can receive information in a timely manner and act quickly on that information if necessary. By comparing information about students' learning behavior styles to information about students' grades, learning analytics research is able to identify which activity patterns are most useful and most effective. , leads to deep learning and provides the best outcomes for students (see Lockyer et al 2013). Researchers using learning analytics are also able to identify patterns of learning behavior that are not helpful to students and lead to failure or dropping out of the course of study. With insights from learning analytics, teachers can identify students who are unlikely to succeed and thus intervene early enough to help students change their learning behavior styles, thereby avoiding bad consequences . Learning analytics provides teachers and students with information that can be useful in identifying potential problems and recommending ways to avoid failure (see Ferguson et al 2012). Students become aware of effective learning behavior styles and teachers have ideas and can guide students in using models that will likely lead to academic success. Learning analytics can also be used as pastoral tools. Information from learning analytics platforms can be helpful in uncovering students who may be experiencing financial, social, medical, emotional, or personal issues. Staff who use learning analytics will be able to provide useful intervention support to students who have personal or emotional needs. Learning analytics is a great tool for providing insights and answers to questions that may never be answered without data. Professors and even students want to make effective decisions and deal with problems decisively (see Ferguson et al 2015). Using learning analytics provides quantifiable insights that can help with strategic decision making. Learning analytics may not provide all the answers, but it could be very helpful in providing strategies and insights that will deepen and improve learning. Online learning at HMS Schools in Kaduna, Nigeria, where I worked as a school principal, began in September 2017. It was jointly developed by our in-house staff and a local IT company. We use a Moodle LMS site. We were able to put around ten courses online on the site for students. The essence of the Moodle site is to enhance and support the face-to-face teaching that our students receive in traditional classrooms. A computer lab was built specifically to support online learning and teaching. Many students and staff enjoy working in the computer lab, but access to Moodle can be from any digital device on or off campus. As a result, staff and students have welcomed Moodle LMS as a useful new online learning environment. With design on Moodle, students and staff canaccess the courses by clicking on the “My courses” block on the website. The “My Courses” tab displays a list of ten courses when clicked. Knowing or understanding student behavior in an online learning environment such as Moodle can be a huge challenge. However, if there is a need to provide an eLearning experience that provides help and support to students in achieving their goals and objectives. Students should benefit optimally from all courses offered at the school, especially when using the blended learning approach (see Scheffel et al 2014). Figure (1) below is a screenshot of the Learning Analytics Enhanced Rubric environment. To Achieve the Goal To get students to learn effectively, one of the most effective ways is to collect data. Learning analytics is an effective approach to data collection and analysis. To effectively integrate the learning analytics approach, we used a plugin for the Moodle LMS called Learning Analytics Enhanced Rubric. Learning Analytics Enhanced Rubric is an example of a descriptive learning analytics tool. As a tool, Learning Analytics Enhanced Rubric uses a range of student data that provides assessment support to the teacher. Teachers are able to assess student performance in various learning and assessment tasks. Learning Analytics Enhanced Rubric generates reports that display performance patterns for all students individually. Teachers can assess students effectively and efficiently in a comprehensive manner using various data. Teachers are able to use the data to evaluate performance indicators (see Scanlon et al (2013). Using the Moodle plugin, Learning Analytics Enhanced Rubric, we were able to collect data, analyze the data and generate insights that have become very useful in our quest to meaningfully engage students in the eLearning experience. With this rubric, information is reported. With the rubric, we have recorded important data. tests and activities carried out on Moodle It has become much easier to check the progress made by students in the courses We can see how many times they log in and how they have participated in the discussion forums on Moodle. a comprehensive overview of student performance and can immediately decide whether to provide additional or additional support. Teachers can also indicate how students are progressing and whether they are likely to succeed in the course. This section greatly facilitates educational analysis and predictions. It has become easier for our teachers to determine which learning materials are most appropriate, relevant or useful. The Learning Analytics Enhanced Rubric provides data in the area of ​​learners' skills, interests, level and performance (see Macfadyen and Dawson (2012). One of the benefits of the Learning Analytics Enhanced Rubric as a learning technology Learning analytics is that it offers rich information about learners' past, present and future performance. This helps teachers plan and personalize teaching so that lessons are more creatively tailored for them. supporting each student in the course Last year, our teachers were able to determine what type of supplemental learning materials to use. This led to higher grades and a much more meaningful learning experience. During the year..