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Mahmoud Abdasalam; Ahmad Alzubi; Kolawole Iyiola – Education and Information Technologies, 2025
This study introduces an optimized ensemble deep neural network (Optimized Ensemble Deep-NN) to enhance the accuracy of predicting student grades. This model solves the problem of different and complicated student performance data by using deep neural networks, ensemble learning, and a number of optimization algorithms, such as Adam, SGD, and RMS…
Descriptors: Grades (Scholastic), Prediction, Accuracy, Artificial Intelligence
Kheira Ouassif; Benameur Ziani – Education and Information Technologies, 2025
The integration of educational data mining and deep neural networks, along with the adoption of the Apriori algorithm for generating association rules, focuses to resolve the problem of misdirection of students in the university, leading to their failure and dropout. This is reached through the development of an intelligent model that predicts the…
Descriptors: Predictor Variables, College Students, Majors (Students), Decision Making
Dalia Khairy; Nouf Alharbi; Mohamed A. Amasha; Marwa F. Areed; Salem Alkhalaf; Rania A. Abougalala – Education and Information Technologies, 2024
Student outcomes are of great importance in higher education institutions. Accreditation bodies focus on them as an indicator to measure the performance and effectiveness of the institution. Forecasting students' academic performance is crucial for every educational establishment seeking to enhance performance and perseverance of its students and…
Descriptors: Prediction, Tests, Scores, Information Retrieval
Asiye Toker Gokce; Arzu Deveci Topal; Aynur Kolburan Geçer; Canan Dilek Eren – Education and Information Technologies, 2025
Artificial intelligence (AI) literacy is critical to shaping students' academic experiences and future opportunities inhigher education. This study examines AI literacy among university students, examining variables such as gender, frequency of use of AI applications, completion of AI-related courses, and field of study. The research involved 664…
Descriptors: Artificial Intelligence, Technological Literacy, College Students, Decision Making
Nesrine Mansouri; Mourad Abed; Makram Soui – Education and Information Technologies, 2024
Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains…
Descriptors: Undergraduate Students, Decision Making, Majors (Students), Specialization
Saleem Malik; K. Jothimani – Education and Information Technologies, 2024
Monitoring students' academic progress is vital for ensuring timely completion of their studies and supporting at-risk students. Educational Data Mining (EDM) utilizes machine learning and feature selection to gain insights into student performance. However, many feature selection algorithms lack performance forecasting systems, limiting their…
Descriptors: Algorithms, Decision Making, At Risk Students, Learning Management Systems