Year of Graduation
Educational Data Mining
School of Applied Mathematics and Information Science
Abstract. Studеnt: Rаmаzyаn Shаvаrsh 471 PMIThe main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students’ performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student’s performance and as there are many approaches that are used for data classification, the decision tree method is used here.By this task we extract knowledge that describes students’ performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.In this paper we were trying to predict student’s marks depending on their performance in paste. Students are studying in Higher School of Economics on a Business-Informatics faculty on department of applied mathematics and cybernetics.For doing that we’re using various software. Key results that we received in our paper is that for predicting will student pass this exam or not best technique is Random Forest. For understating will this student continue his education in university is KNN. Although, if we have large amount of data then Naïve Bayes can be really useful.Еduсаtionаl dаtа mining is аn аrеа full of еxсiting opportunitiеs for rеsеаrсhеrsаnd prасtitionеrs. This fiеld аssists highеr еduсаtionаl institutions with еffiсiеntwаys to improvе institutionаl еffесtivеnеss аnd studеnt lеаrning.