NotesFAQContact Us
Collection
Advanced
Search Tips
Back to results
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1450595
Record Type: Journal
Publication Date: 2024-Nov
Pages: 25
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1360-2357
EISSN: EISSN-1573-7608
Available Date: N/A
Feature Optimization and Machine Learning for Predicting Students' Academic Performance in Higher Education Institutions
Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf
Education and Information Technologies, v29 n16 p21169-21193 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching. ICT-based learning generates a huge amount of data that can be utilized to better understand student behavior and improve students learning. Predicting students' academic performance is essential to take proactive measures to improve student learning and reduce the risk of student dropout and failure. This study aims to use video learning analysis and data mining approaches to predict student academic achievement and identify the factors affecting their performance. For this purpose, the dataset containing records of 326 students from a higher education institution (HEI) is used which contains records from SIS, Moodle, and eDify. This study advocates the use of a balanced dataset and optimized feature set to obtain better performance for students' academic performance prediction. Several machine learning and deep learning models are applied to analyze their performance against the original dataset, balanced dataset, and balanced dataset with the optimized feature set. Experimental results demonstrate decision tree classifier outperforms with an accuracy of 99.06% with a balanced dataset and optimized feature set. Further analysis indicates that the video interaction feature has a strong impact on the performance of students.
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link-springer-com.bibliotheek.ehb.be/
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