Big data and traditional quantitative methodologies in education collide in school analytics. The collection and analysis of data about learners and their settings to understand and enhance learning outcomes are referred to as learning analytics. Data about learners and how they learn is being collected by governments, colleges, testing organisations, and massive open online course providers. However, until the relatively recent discovery of the means and instruments, most of that data remained largely unexplored.
Consider educational data as a machine that uses data to aid in the educational process, producing outputs like progress, success, and achievement. The parent, teacher, student, and administration all impact how data is used. These data could be evaluated and used in a variety of ways. Here are a few examples:
- Analyzing student data allows teachers to obtain a better understanding of a student’s learning abilities and challenges, as well as assist a culturally formed process that uses specific inputs to create best results.
- Specific data inputs can range from teacher qualifications to student demographics, with attendance, grades, assessment scores, and graduation rates as specific data outputs.
- When data is properly analysed, it can be used to detect particular student needs and differentiate educational strategies.