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

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Abstract

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.

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Availability of data and materials

The data of this study “EduRisk” is available from the corresponding author upon reasonable request through bi-dac.com/download

Abbreviations

Acc:

Accuracy

AdaBoost:

Adaptive Boosting

AI:

Artificial Intelligence

CP:

Checkpoints

DL:

Deep Learning

DS:

Dataset Features

EF:

Embedded Features

GCN:

Graph Convolution Neural Network

HF:

Historical Features

KG:

Knowledge Graph

KGE:

Knowledge Graph Embeddings

LGBM:

Light Gradient Boosting Machine

LR:

Linear Rregression

MAE:

Mean Absolute Error

ML:

Machine Learning

MSE:

Mean Squared Error

NoSQL:

Not Only SQL

PCA:

Principal Component Analysis

RF:

Random Forest

SVM:

Support Vector Machine

TG:

Total Grade

TPR:

True Positive Rate

HW:

Homework assignment

LGB:

Light Gradient Boosting

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, 15551, Abu Dhabi, United Arab Emirates

    Balqis Albreiki, Tetiana Habuza, Nishi Palakkal & Nazar Zaki

  2. Office of Institutional Effectiveness, United Arab Emirates University, Al Ain, 15551, Abu Dhabi, United Arab Emirates

    Balqis Albreiki

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  1. Balqis Albreiki
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  2. Tetiana Habuza
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  3. Nishi Palakkal
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  4. Nazar Zaki
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Correspondence to Balqis Albreiki.

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Albreiki, B., Habuza, T., Palakkal, N. et al. Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students. Educ Inf Technol 29, 6791–6820 (2024). https://doi.org/10.1007/s10639-023-11938-8

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