A Comparison of Machine Learning Techniques for the Prediction of the Student’s Academic Performance

Predicting the academic performance of students has been an important research topic in the Educational field. The main aim of a higher education institution is to provide quality education for students. One way to accomplish a higher level of quality of education is by predicting student’s academic performance and there by taking earlyre- medial actions to improve the same. This paper presents a system which utilizes machine learning techniques to classify and predict the academic performance of the students at the right time before the drop out occurs. The system first accepts the performance parameters of the basic level courses which the student had already passed as these parameters also influence the further study. To pre- dict the performance of the current program, the system continuously accepts the academic performance parame- ters after each academic evaluation process. The system employs machine learning techniques to study the aca- demic performance of the students after each evaluation process. The system also learns the basic rules followed by the University for assessing the students. Based on the present performance of the students, the system classifies the students into different levels and identify the students at high risk. Earlier prediction can help the students to adopt suitable measures in advance to improve the per for- man ce. The systems can also identify the factor saffecting the performance of the same students which helps them to take remedial measures in advance.

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Predicting students’ academic performance using a modified kNN algorithm

Pollack Periodica ◽

10.1556/606.2021.00374 ◽

2021 ◽

Author(s):

Moohanad Jawthari ◽

Veronika Stoffová

Keyword(s):

Machine Learning ◽

Academic Performance ◽

Nearest Neighbors ◽

Machine Learning Techniques ◽

Categorical Variables ◽

Ratio Scale ◽

K Nearest Neighbors ◽

Learning Techniques ◽

Nominal Variables ◽

Better Than

AbstractThe target (dependent) variable is often influenced not only by ratio scale variables, but also by qualitative (nominal scale) variables in classification analysis. Majority of machine learning techniques accept only numerical inputs. Hence, it is necessary to encode these categorical variables into numerical values using encoding techniques. If the variable does not have relation or order between its values, assigning numbers will mislead the machine learning techniques. This paper presents a modified k-nearest-neighbors algorithm that calculates the distances values of categorical (nominal) variables without encoding them. A student’s academic performance dataset is used for testing the enhanced algorithm. It shows that the proposed algorithm outperforms standard one that needs nominal variables encoding to calculate the distance between the nominal variables. The results show the proposed algorithm preforms 14% better than standard one in accuracy, and it is not sensitive to outliers.

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