| dc.description.abstract | Nowadays, With the digitization of the systems in the universities, academic institutions
create a large volume of student-related data using computerized form. For the exploration of
hidden information from this golden data, a lot of techniques and tools, introduced in recent
years, help the data analysis process become easier than ever before. In higher education
systems, applying a data driven system can help them take advantage of this data resource for
the research of talent or final year students, and scientists in many departments to analyze
behaviors, affecting factors… Instead of wasting the resources for storing useless data, This
can be a new approach to have general and multi-dimensional insights to improve the
education qualification day by day. The changes are based on the actual data of the university
itself.
Along with the data driven system, Data mining, which is used as an analytical tool, becomes
essential for these departments to transfer substantial amounts of data and manipulate it into
useful knowledge. Therefore, educational data mining techniques were created for
constructing predicting or classifier models built from the student historical records. In this
context, automated systems supporting the lecturer are needed. One significant problem is not
being able to predict a student's academic grade in a course, which would help them to
achieve better results in the future course. Therefore, the main goals of this research are to
explore and implement the efficiency of machine learning in the field of Educational data
mining, especially in predicting aspects of student’s performance, including Grade prediction,
Dropout or Course recommendation system in advance… | en_US |
