Analytics have always been used in the business sector to identify consumer trends, but learning analytics has become an important technology for the education sector (see Campbell et al, 2007 , Colvin et al, 2015, Cooper, 2012a ). Some researchers have viewed learning analytics as “big data” applied to education. Many reports have indicated that learning analytical terminology owes its name to analytics applied in business (see also Dawson et al, 2014, Dyckhoff, 2013). Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Learning analytics is essentially data-driven. So interest in learning analytics is really interest in “Big Data”. Big data refers to the collection of large, complex data sets that are difficult to process using traditional data processors (see also Dawson et al, 2014). This is information generated by and for educational contexts. Over the years, there has been an increase in the use of analytics across different industries to solve problems and provide solutions, especially to determine trends in different businesses. Companies want to get ideas on purchasing patterns to realign their marketing campaigns and make them more effective. Some companies want to identify individuals' spending patterns and know how to manage inventory levels (Campbell et al, 2007). As an approach, Learning Analytics applies data analytics principles to student learning (Showers 2014). The goal is to offer actionable and accurate insights into the learning process through the aggregation, modeling and exploration of essential data sources and to offer evidence that learning has been improved (Ferguson et al 2017). Learning analytics uses data from different sources for its research. Data can be collected from student attendance, student library use, student information systems, student participation in online forums, and even student biometric information, as well as data on how students interact in the virtual learning environment (see Ferguson 2012).VLEs or virtual learning environments are important sources of data for learning analytics. Popular VLEs or online platforms such as Sakai, Moodle, and Blackboard have become huge sources of data for learning analytics. The VLEs contain huge resources for tutors and teachers. Through VLEs, students access previous exam questions, notes, books and other teaching resources. VLEs are very important platforms and have become the mainstay of education across the world. VLEs have therefore become important mines of actionable data sources for learning analytics researchers. VLEs provide information on how students access resources, which resources they access, the time students spend on resources, and the resources students use most often. Therefore, VLEs provide information about students' learning behavior patterns and indicate how the pattern is developed or can be developed. With the help of learning analytics, teachers have valuable information and insights into the resources their students access, how they use the information, and their students' level of online activity. Students can also get information about how committed they are to their studies compared to their classmates. Learning analytics offers real-time insights. In this way, both the teachers and thestudents can receive timely information and, if necessary, act in advance based on the information. By comparing information about students' learning behavior styles with information about students' grades, learning analytics research can identify which activity patterns are most useful and effective, leads to deep learning, and offers the better 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 withdrawal from the course of study. With information from learning analytics, teachers can determine which students are unlikely to succeed and therefore can intervene early enough to help students change their learning behavior styles thus avoiding negative consequences. Learning analytics offers teachers and students 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 insights and can guide students to use models that are likely to 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 using 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 except with data. Teachers and even students want to make effective decisions and tackle problems decisively (see Ferguson et al 2015). Using learning analytics provides quantifiable information that can aid in strategic decision making. Learning analytics may not provide all the answers, but it may be helpful in offering strategies and information that will deepen and improve learning. E-learning at HMS Schools, Kaduna, Nigeria, where I worked as a principal, started in September 2017. It was jointly developed by our internal staff and a local IT company. We use a Moodle LMS site. We managed to upload about ten courses for students to the site. The essence of the Moodle site is to enhance and support the face-to-face education our students receive in traditional classrooms. A computer laboratory was created specifically to support e-learning and teaching. Many students and staff prefer to work in the computer lab, but Moodle can be accessed from any digital device on or off campus. Therefore, both staff and students have welcomed the Moodle LMS as a new and useful online learning environment. With design on Moodle, students and staff can access courses by clicking on the 'My Courses' block on the site. The "My Courses" tab displays a list of all ten courses when clicked. Knowing or understanding student behavior in an online learning environment like Moodle can be a huge challenge. However, if you need to provide an eLearning experience that provides help and support to students to achieve their goals and objectives. Students should benefit optimally from all courses offered at school, especially when using the approach ofblended learning (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 efficient ways is to collect data. Learning analytics is an effective approach for collecting and analyzing data. To effectively incorporate 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 variety of student data that provides assessment support for the teacher. Teachers are able to evaluate students' performance in different learning and assessment activities. The advanced Learning Analytics rubric generates reports that show the performance patterns of all students individually. Teachers can efficiently and effectively evaluate students holistically 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 information which became very useful in our attempt to engage students meaningfully in the eLearning experience. With this rubric we recorded important data. This included students' scores on tests and activities carried out on Moodle. It has become much easier to ascertain the progress students are making in their courses. We can see how many times they have logged in and how they have participated in discussion forums on Moodle. Teachers have a complete overview of student performance and can decide immediately whether they need to provide students with extra or additional support. Teachers can also indicate student progress and whether they are likely to pass the course. This rubric makes educational analysis and forecasting much easier. It has become easier for our teachers to tell which teaching materials are most appropriate, relevant or useful. The Learning Analytics Enhanced Rubric provides data in the area of ​​student skills, interests, level and performance (see Macfadyen and Dawson (2012). One of the advantages of the Learning Analytics Enhanced Rubric as a learning analytics technology is that it offers in-depth insights into students. past, present and future performance. This helps teachers plan and personalize instruction so that lessons are more creatively tailored to support individual students in the course year, our teachers have been able to determine the type of supplementary teaching materials to use This has led to higher grades and a much more meaningful learning experience Over the past year, our teachers in HMS schools have gained experience in the area of ​​personalized lessons now teach individually instead of teaching in classes, so lessons are personalized to provide rich learning experiences for each individual student. When there is an indication that students are struggling or struggling, teachers immediately provide customized eLearning resources and educational tools to prevent problems. performance. This could be providing helpful websites, videos or books. With the use of this learning technology, our teachers demonstrate that every learning experience is worthwhile and should.