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.