Student Individualized Growth Model and Assessment (SIGMA)

As I mentioned in my previous posts student dropout from educational programs is becoming the most pressing issues in current time. According to the Carnegie Corporation of New York they said that 

“Today, young people who leave high school without excellent and flexible reading and writing skills stand at a great disadvantage. In the past, those students who dropped out of high school could count on an array of options for establishing a productive and successful life. But in a society driven by knowledge and ever-accelerating demands for reading and writing skills, very few options exist for young people lacking a high school diploma.”

Same like the Sakai Learning Management System Microsoft have introduced a new learning analytic platform which can be used identify different students according to their performance activities which is basically using the predictive analysis approach. 

According to the study conducted by the U.S. Department of Education the most common reasons for student to be dropping out of school are 

• Lack of educational support. Many students decided to drop out of high school due to lack of sufficient parental support and educational encouragement.
• Outside influences.  
• Special needs. Students often drop out of high school because they require specific attention to a certain need, such as dyslexia or other learning disabilities
• Financial problems.  

Out of those four mentioned above the Lack of educational support and the Special needs reasons can be easily managed using predictive analysis approach since student who are at risk of failing can be identified at early stage by analyzing their historical data of learning behavior. 

According to the Microsoft the Student Information Systems and Learning Management Systems (LMS) have a  shortcoming of inability to perform integrated analysis of large amounts of data. As they mentioned in their report to manage this type of reporting infrastructure requires a different type of data analysis system that is highly optimized for rapid and comprehensive analysis of large amounts of data. Online Analytical Processing can be used as an approach for fast analysis of large amounts of data offering greater insight into student performances.

Microsoft Education Analytics Platform (EAP) or SIGMA offers both business intelligence and predictive analytics data management services which consider about different aspect of the students where it categorize the influence factors in to individual and family. Based on these factors they use predictive analysis approaches to identify the students who are at risk of dropping out from the educational programs.

Reference : Student Individualized Growth Model and Assessment (SIGMA), A Microsoft Education Analytics Platform Approach to Students at Risk, May 2010