Education
SuperSTAR is used by many educational institutions to analyze statistics about education and educational programs for minority/under privileged students.
The SuperSTAR suite of products is also used for educational planning and performance monitoring of schools, technical institutions, and universities. The unique microdata (student level) access capabilities of SuperSTAR combined with the easy-to-use query front-ends enable unfettered access to the data. They enable educational institutions or agencies to ‘ask any question’, or pursue any line of research.
Educators use SuperSTAR's analytic capabilities to query across student-level data and to perform longitudinal analysis to segment student populations over school years. For example, using SuperSTAR educators can analyze the performance of a particular segment of the school population and track their grades semester by semester, year by year.
All the data is available all the time through simple click-and-drag interfaces. This ease of use coupled with the high performance database engine enables educators to perform ‘what if’ analysis and uncover patterns in the data that would otherwise remain hidden. For example, educators can uncover class enrollment patterns to reveal relationships, and then use the information to better plan school curriculums.
Types of Educational Data
Educational data takes many different forms, and is usually characterized as being complex, large, and sensitive. Typical datasets include:
- school roll demographics
- individual student performance/reports
- institution performance metrics
- course enrolments
- educational funding data.
It is now common for student performance to be
monitored from pre-school right through to tertiary and vocational education. This has led to a rapid increase in the volumes of extremely rich and valuable student data, resulting in issues about how to deal with such large datasets.
SuperSTAR – SAFER Access to Student-Level Information
SuperSTAR provides direct access, via a simple hierarchical display, to the lowest level of data whether it be
student test results or individual student records over the years. There is no need to summarize the data and lose granular access as is the case with many traditional OLAP systems. And because data is stored at the lowest level, it is possible to create groups of information that contain specific low-level details such as student record type, result, and time.
Privacy protection can be by applied to data to ensure its confidentiality before it is presented to the user. So whilst the information presented to the user is derived from records about students and their exam results, the students' personal data is never revealed to anyone unless they have the appropriate access rights and permissions.