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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Ekaterina Fadeeva
Using Machine Learning Technologies in Educational Practices
Business Informatics
(Bachelor’s programme)
8
2017
Machine learning is a broad subfield of artificial intelligence which provides computers with methods of building algorithms that are able to learn without being explicitly programmed. The area of machine learning application is appeared to be constantly expanding due to accumulation of huge amounts of data in science, business, healthcare, education and other spheres.

This research is intended to demonstrate the potential of machine learning techniques to acquire new knowledge from demographic and academic data of students collected by universities in order to reduce the number of students dismissed from the university because of the academic failure.

Three major tasks were done in order to achieve this goal. First of all, there were defined the basic concepts of machine learning such as types of problems and tasks, approaches and spheres of application. Secondly, there were overviewed existing practices of implementation of machine learning algorithms in education field by different universities, massive open online course platforms and schools.

Finally, a case study for predicting students’ performance in а certain discipline based on their pre-university characteristics and previous academic achievements was presented. This predicting algorithm is a machine learning classification model, which learns to divide students into two classes: those who tend to pass the exam and those who are at great risk of failing it. The predicting algorithm received a dataset of 583 students and used 80% of the dataset to learn and other 20% to test its predictive ability. The algorithm showed 95% accuracy rate.

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