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ERIC Number: EJ1344010
Record Type: Journal
Publication Date: 2022
Pages: 14
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1550-1876
EISSN: EISSN-1550-1337
Available Date: N/A
Developing and Comparing Data Mining Algorithms That Work Best for Predicting Student Performance
International Journal of Information and Communication Technology Education, v18 n1 Article 35 2022
Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decisions. Identifying potential at-risk students may help instructors and academic guidance to improve the students' performance and the achievement of learning outcomes. The aim of this research study is to predict at early phases the student's failure in a particular course using the standards-based grading. Several machine learning techniques were implemented to predict the student failure based on support vector machine, multilayer perceptron, naïve bayes, and decision tree. The results on each technique show the ability of machine learning algorithms to predict the student failure accurately after the third week and before the course dropout week. This study provides a strong knowledge for student performance in all courses. It also provides faculty members the ability to help at-risk students by focusing on them and providing necessary support to improve their performance and avoid failure.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A