Can we use Learning Analytics Approaches to Prevent Dropping out of Students ???

In any educational institution the learning capacities and the learning capabilities of the students are varying in different levels due to the way their arranging their learning behaviors. Due to this learning nature applying the same teaching principle on each and every student in the same manner does not provide the required level of knowledge in students. This brings a indication to the teachers why many students are dropping out from schools or educational institutions. 

In year 2009, Massachusetts Department of Elementary and Secondary Education started a project known as Dropout Prevention Planning Project to implement an approach to understand the different factors on student's dropout incidents. They implemented a Student Information Management System which contains information about the student within the State and they initiate an index known as Early Warning Indicator Index for measure the risk of dropout in each student. 

According to the Cash, Dawicki, Sevick [2011] the early warning system to be useful and effective and it should allow districts to achieve the following goals and objectives in their dropout prevention efforts:

• Goal 1: Accurately define and uncover students’ problems and needs
• Goal 2: Successfully identify interventions and improvement strategies
• Goal 3: Effectively target and initiate programs and reforms
• Goal 4: Truthfully monitor ongoing efforts and progress with at-risk students

In order to achieve these goals either Business Intelligence or Predictive Analytics can be used. Business intelligence model focus on analyzing historical and current data in order to provide a look at operations or conditions at a given period of time. Predictive analytics approach attempts to incorporate historical data into statistical models in order to make predictions about future events or outcomes.

According to them currently there are several early warning systems available which are using predictive analysis approaches to analyze the students. Microsoft SIGMA is a early warning system  which capitalizes on its Education Analytics Platform (EAP) to provide a new data-based approach to managing students who are at-risk and it is known as the Student Individualized Growth Model and Assessment. 

Mizuni’s Data Warehouse and Dashboard Suite is a transactional and aggregation data store for managing and analyzing data and offers education stakeholders insight into student performance by monitoring key indicators to increase student achievement.

VERSI-FIT is also based on Microsoft has also developed its own early warning system based upon the Education Analytics Platform which is known as the Edvantage At-Risk Early Warning System and Credit Recovery System.

Reference: T. Cash, C. Dawicki, and B. Sevick, “Springfield Public Schools Dropout Prevention Program Assessment & Review (PAR),” 2011.