Abstract
Educational Intelligence is a broad area of big data analytical applications that make use of big data technologies for implementation of solutions for education and research. This paper demonstrates the designing, development and deployment of an educational intelligence application for real-world scenarios. Firstly, a quality assessment framework for higher education systems that evaluate institutions on the basis of performance of outgoing students was proposed. Secondly, big data enabled technological setup was used for its implementation. Literature was surveyed to evaluate existing quality frameworks. Most existing quality assessment systems take into account the dimensions related to inputs, processes and outputs, but they tend to ignore the perspective that assesses the institution on the basis of outcome of the educational process. This paper demonstrates the use of outcome perspective to compute quality metrics and create visual analytics. In order to implement and test the framework, R programming language and a cloud based big data technology that is Google, BigQuery were used.
Highlights
The concept of educational intelligence [1] was introduced as an umbrella term for all analytical solutions created for education and research sectors
A range of applications have been proposed in recent times, ranging from applications for improving the operational efficiency of educational and research institutes, to specific predictive analytical applications for foretelling student dropout rates and prescriptive analytical solutions to improve the quality of education
Profiles of students passing out each year are scanned for computing quality scores and quality score data per year is stored for generating time-based analytics
Summary
The concept of educational intelligence [1] was introduced as an umbrella term for all analytical solutions created for education and research sectors. A range of applications have been proposed in recent times, ranging from applications for improving the operational efficiency of educational and research institutes, to specific predictive analytical applications for foretelling student dropout rates and prescriptive analytical solutions to improve the quality of education. This paper proposes an outcome-based quality assessment framework for higher education systems. It demonstrates the implementation of an educational intelligence solution with the help of base technologies used for big data storage and processing. Effective management and quality assessment of the higher education system is not just important, but it is necessary. The concept of quality in higher education has found varied definitions and descriptions in literature.
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