Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students

被引:4
作者
Albreiki, Balqis [1 ,2 ]
Habuza, Tetiana [1 ]
Palakkal, Nishi [1 ]
Zaki, Nazar [1 ]
机构
[1] United Arab Emirates Univ, Coll Informat Technol, Dept Comp Sci & Software Engn, Al Ain 15551, Abu Dhabi, U Arab Emirates
[2] United Arab Emirates Univ, Off Inst Effectiveness, Al Ain 15551, Abu Dhabi, U Arab Emirates
关键词
Students' performance; Knowledge graphs; Machine learning; Clustering; Educational ranking; Personalized learning; PERFORMANCE; SYSTEMS;
D O I
10.1007/s10639-023-11938-8
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
The nature of education has been transformed by technological advances and online learning platforms, providing educational institutions with more options than ever to thrive in a complex and competitive environment. However, they still face challenges such as academic underachievement, graduation delays, and student dropouts. Fortunately, by harnessing student data from institution databases and online platforms, it becomes possible to predict the academic performance of individual students at an early stage. In this study, we utilized knowledge graphs (KG), clustering, and machine learning (ML) techniques on data related to students in the College of Information Technology at UAEU. To construct knowledge graphs and visualize students' performance at various checkpoints, we employed Neo4j-a high-performance NoSQL graph database. The findings demonstrate that incorporating clustered knowledge graphs with machine learning reduces predictive errors, enhances classification accuracy, and effectively identifies students at risk of course failure. Additionally, the utilization of visualization methods facilitates communication and decision-making within educational institutions. The combination of KGs and ML empowers course instructors to rank students and provide personalized learning interventions based on individual performance and capabilities, allowing them to develop tailored remedial actions for at-risk students according to their unique profiles.
引用
收藏
页码:6791 / 6820
页数:30
相关论文
共 93 条
[1]   Prediction of Student's performance by modelling small dataset size [J].
Abu Zohair, Lubna Mahmoud .
INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION, 2019, 16 (01)
[2]  
Acharya Anal., 2014, International Journal of Computer Applications, V107
[3]   Data mining approach to predicting the performance of first year student in a university using the admission requirements [J].
Adekitan, Aderibigbe Israel ;
Noma-Osaghae, Etinosa .
EDUCATION AND INFORMATION TECHNOLOGIES, 2019, 24 (02) :1527-1543
[4]  
Ahmad Z., 2018, Bulletin of Education and Research, V40, P157
[5]   Using Educational Data Mining Techniques to Predict Student Performance [J].
Al Breiki, Balqis ;
Zaki, Nazar ;
Mohamed, Elfadil A. .
2019 INTERNATIONAL CONFERENCE ON ELECTRICAL AND COMPUTING TECHNOLOGIES AND APPLICATIONS (ICECTA), 2019,
[6]  
Al-Shehri H, 2017, CAN CON EL COMP EN
[7]   Extracting topological features to identify at-risk students using machine learning and graph convolutional network models [J].
Albreiki, Balqis ;
Habuza, Tetiana ;
Zaki, Nazar .
INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION, 2023, 20 (01)
[8]  
Albreiki B, 2022, INT J EDUC TECHNOL H, V19, DOI 10.1186/s41239-022-00354-6
[9]   Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions [J].
Albreiki, Balqis ;
Habuza, Tetiana ;
Shuqfa, Zaid ;
Serhani, Mohamed Adel ;
Zaki, Nazar ;
Harous, Saad .
BIG DATA AND COGNITIVE COMPUTING, 2021, 5 (04)
[10]  
Aleem A, 2020, INT CONF COMM SYST, P182, DOI [10.1109/CSNT.2020.35, 10.1109/CSNT48778.2020.9115734]