Evidence of Students’ Academic Performance at the Federal College of Education Asaba Nigeria: Mining Education Data

Ojugo, Arnold Adimabua
Odiakaose, Christopher Chukwufunaya
Emordi, Frances
Ako, Rita Erhovwo
Adigwe, Winifred
Anazia, Kizito Eluemonor
Geteloma, Victor

Knowledge Engineering and Data Science

One main objective of higher education is to provide quality education to its students. One way to achieve the highest level of quality in the higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, and prediction about students’ performance. The knowledge is hidden among the educational data set and is extractable through data mining techniques. The present paper is designed to justify the capabilities of data mining techniques in the context of higher education by offering a data mining model for the higher education system in the university. In this research, the classification task is used to evaluate student’s performance, and as many approaches are used for data classification, the decision tree method is used here. By this, we extract data that describes students’ summative performance at semester’s end, helps to identify the dropouts and students who need special attention, and allows the teacher to provide appropriate advising/counseling.