A survey of the research on forecasting pupils' academic achievement using machine learning approaches.
Abstract
The amount of student data recorded in educational databases is continually expanding. These databases include hidden patterns as well as essential data about students' behaviour and performance. The most efficient way for analysing stored educational data is data mining. Educational data mining (EDM) is the practise of analysing massive volumes of educational data using various data mining techniques in educational settings. Several researchers used various machine learning algorithms to analyse student data and extract hidden information. Predicting pupils' academic achievement is required in educational settings to assess the quality of the learning process. As a result, it is one of the most prevalent EDM applications. We give an overview of data mining techniques, EDM and its applications, and explore them in this survey study.
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