Improving Student Academic Performance Prediction Models using Feature Selection

Students’ academic achievement plays a significant role in the polytechnic institute. It is an important task for the technical student to achieve good results. It becomes more challenging by virtue of the huge amount of data in the polytechnic student databases. Recently, the lack of monitoring of academic activities and their performance has not been harnessed. This is not a good way to evaluate the academic performance of polytechnic students in Bangladesh at present. The study on existing academic prediction systems is still not enough for the polytechnic institutions. Consequently, we have proposed a novel technique to improve student academic performance. In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment, and target_ marks. By using the proposed model, we can more effectively improve polytechnic student achievement and success.

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Role of FCBF Feature Selection in Educational Data Mining

Mehran University Research Journal of Engineering and Technology ◽

10.22581/muet1982.2004.09 ◽

2020 ◽

Vol 39 (4) ◽

pp. 772-778

Author(s):

Maryam Zaffar ◽

Manzoor Ahmad Hashmani ◽

K.S. Savita ◽

Syed Sajjad Hussain Rizvi ◽

Mubashar Rehman

Keyword(s):

Data Mining ◽

Feature Selection ◽

Prediction Model ◽

Student Performance ◽

Performance Prediction ◽

Prediction Models ◽

Educational Data Mining ◽

Action Plans ◽

Factors Affecting ◽

Academic Organization

The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.

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