How we can implement Learning Analytics???

Academic analytics or Learning analytics is a wide term used recently to describe the use of data mining in educational data sources. Researches have used various data mining methodologies in different ways to understand or identify the learning models and learning patterns of the students. Various learning management systems can be used for providing the study programs for the students those who can be connected in open distance mode or either in a much more hybrid manner. But applying the same teaching principle on all the students in the same way will not cope with the ultimate goal of any learning management systems available which is to help the students to learn rather helping them to pass.

By understanding the way these students are learning it will help the teachers and the academics to change the teaching methodologies they used to cope with the requirements of the students. In order to identify how these students are behaving performance models can be used. It can be used as a monitoring tool to take necessary actions for the issues related to student learning. As the academic analytics suggest the knowledge can be created about the students by applying the statistical analysis and predictive modeling on educational data sources.

For building the predictive model we can use the continue streams of data which is created within the learning management systems with the data mining techniques and it can be used as a decision making tool for teachers. Before building the predictive model it is a must to acquire proper information out from the student data since not all information available in the data sources is important for the analyzing. The outcomes of these prediction models will be to provide guidelines or forecasting about the event that can be occurring based on the observations or the scenarios.

In any data mining research some steps can be given as the basic steps that we need to follow in the knowledge discovery process. The same principles can also be applied in the learning analytics processes where the data collecting and data pre-processing steps will be continued in the same manner. According to the requirements in the data mining methodology data reductions or data cleaning steps can be done. After the prediction model is been implemented using data mining approaches it needs to verify that the predictive accuracy is acceptable on the given student data. The main advantage which I see in the given context is that most of the data which are available in the data sources are labeled and they contains information about the student characteristics as well as the course management events where the modeling process can be applied in many ways.