Student Information Systems help in assessments and data tracking. Learn how

EducationZen

Student Information system or SIS is an information system that manages student information and organizes it for various school processes. This data is used to carry out many day to day activities like fee management, report generation, accounting and budgeting, etc. The system may range from simple data capturing one to a full-fledged school ERP system with data mining capabilities.

There is huge amount of student data captured by educational institutions through SIS. It is estimated that more than 1 million records are generated for every 10,000 students. This is only going to increase with more school and colleges embracing school management systems.

Use of this big data does not have to be limited to routine school administration. It can be applied to know about student learning, score patterns, at-risk students and much more. The application of data analytics in SIS opens up many ways to improve school management and student learning.

To understand how SIS can help in assessment and tracking, it is important to understand what the system captures.  Student Information systems have evolved into a sophisticated data capturing systems that can track:

  1. Student attendance
  2. Admission and re-enrollment
  3. Student profile including assessment reports, medical history, demographics, learning activities and disciplinary records
  4. Student progress reports through the year
  5. Teacher activity and records
  6. Communication between school and parents
  7. Some systems may import data from public records and internet.

Processing above information can give important information on how to improve student learning. Interesting data visualization makes it easy to understand and take decisions.

Here is how SIS can help in assessments and data tracking

  • Track student performance

 Student results stored throughout the year can be analyzed to track their performance. These can be viewed at school level, class level or at individual student level. This also helps to assess the effectiveness of classes, lectures and virtual learning models if used by the school.

This information can be used to make changes to learning modules or mode of teaching.

  • Factors of success

Data analytics can shed light on factors that are most important to student performance. For example, University of Wolverhampton found that the distance students live from campus is highly correlated to student success ratio.

Similarly, factors like system log in frequency, social background, past scores can all be determinants of graduation success.

Schools can work to improve the probability of student success by controlling these factors once they are known.

  • Teacher assessment

SIS also captures data on teachers. This includes average student test scores for teacher, activity on virtual learning module (or VLE), student engagement and parent-teacher communication.

Tracking this data can point to teacher’s effectiveness and areas of improvement.

  • Predict student performance

Predictive analysis can indicate future student performance and identify at-risk students.  This is done by looking for early red-flags which may in the form of absenteeism or low grades. This foresight can go a long way in helping students at the right time and lowering school dropout rates.

  • Track intervention success

Schools can design corrective course of action once the above assessment is done. This could be in the form of extra classes, teacher training or changes in delivery of curriculum.

Data on student performance after the intervention will point to effectiveness of the program. The success can be measured by increased scores or lower drop outs.

A lot can be achieved with the management of data in student information systems. Schools that track data and use it, get a better overview of what is happening with student learning.

Data analytics and predictive analytics transform huge amounts of raw data into actionable strategies through assessment.

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