Can we Classify students with Data Mining ???

In web-based educational environments predict student’s performances is very important where the students who are at the risk of failing examinations can be identified at the early stage of the course modules and the educators can take necessary actions to improve their knowledge to a more higher level and to increase their learning capacities as well. 

In data mining context use of classification on the educational data is a upcoming research area where to discover potential student groups with similar characteristics and to identify learners with low motivation and find corrective actions to lower drop-out rates. 

C. Romero, S. Ventura, P. G. Espejo, and C. Hervás [2008] have tried to used different classification approaches on  the student data to compare the applicability on data mining techniques for classifying the students in to groups and to predict the final marks obtained in the course modules. 

In their research they used a framework which is known as KEEL which is an open source framework for building data mining models including classification, regression, clustering, pattern mining and based on this framework they developed an data mining tool which can be integrated in to the moodle environment. 

Using Clustering or Association rules on Educational Data

In any learning management system or e-learning environment student accessibility to the study materials are varying in a very diverse manner. Many student are expecting study materials which can be easily understandable so that they can gather more knowledge through them. In order to provide the expected level of learning for the students identifying the learning behavior of each student is required. Due to this nature of different learning patterns of each student, teachers or tutors should be able to provide the materials much more pervasive manner. 

J. Mamcenko, I. Sileikiene, J. Lieponiene, R. Kulvietiene [2011] have tried to apply data mining methodologies to discover the different learning patterns of each students. In their approach they used clustering and association rule mechanisms to identify the learning patterns. 

For the research they focused their research on a programming course examination and the ways that the students have answered. Their collected data on questions given for the student and the answers given by the students with the amount of time that each student has spent on each question to construct the dataset. For the clustering mechanism they used Kohonen algorithm which is based on a self-organizing map (SOM) or self-organizing feature map (SOFM) that is a type of artificial neural network (ANN) and for the association rule mining they used Simultaneous Depth-First Expansion  (SIDE) algorithm.

The analysis of this research indicated that the students find more difficulties in understanding the course development policies. Another indication they found is that the student who are spending more time on questions answered them incorrectly than the questioned they answered in lesser time.

According to their research it can be concluded that the clustering mechanism they used can be used to discover the statistical information about the student behaviors and the learning patterns and the association rules are better way of identifying the complex rules behind student behaviors on e - examination data.   

Reference : J. Mamcenko, I. Sileikiene, J. Lieponiene, R. Kulvietiene, Analysis of E-Exam Data Using Data Mining Techniques, 2011

What is the correct way in learning ??? Is there a way ???

In educational context learning is not only applying to the students, it also applies to the organizations where to use the information and learning processes effectively and efficiently drive learning and development.

According to the research ‘The C-Level and the Value of Learning’, Tony O’Driscoll, Brenda Sugrue and Mary Kay Vona the IBM looked at learning as a provider of strategic value to organizations to accelerate growth, enable transformation and increase productivity.

Turtle Rattle Performance/Dynamic Knowledge Alliance (TRP/DK) have taken a more holistic and integrated approach to aligning learning to performance that is based on scientific-based management and instructional practice. In their approach they introduced an model known as Learning Light Learning Performance Value Analysis (LPVA) maturity model which can be used as a drive way to arrange the level of learning towards the required performance.

Learning Performance Value Analysis reflects an organizations ability to execute performance driven learning effectively and efficiently to meet current strategic business needs as well as creating capability for the future. Performance driven learning ensures that investment in learning activities is always focused on vital performance elements and these elements are focused on performance factors that are critical to a learning scenario.

Based on the indications through this model the organization can identify the level of learning capacity they currently in and manage it appropriately as needed. This will ensure that the learning processes within the organization always focuses on the predictive performance level and to achieve it by dynamically changing its drive ways accordingly. 

Reference : Debbie Carlton and Jim Lacey, Aligning Learning to Performance, Learning Light ‘Active Learner’ Series, 2007

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

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.