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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
Tenzin Doleck; Pedram Agand; Dylan Pirrotta – Education and Information Technologies, 2025
As is rapidly becoming clear, data science increasingly permeates many aspects of life. Educational research recognizes the importance and complexity of learning data science. In line with this imperative, there is a growing need to investigate the factors that influence student performance in data science tasks. In this paper, we aimed to apply…
Descriptors: Prediction, Data Science, Performance, Data Analysis
Nehyba, Jan; Štefánik, Michal – Education and Information Technologies, 2023
Social sciences expose many cognitively complex, highly qualified, or fuzzy problems, whose resolution relies primarily on expert judgement rather than automated systems. One of such instances that we study in this work is a reflection analysis in the writings of student teachers. We share a hands-on experience on how these challenges can be…
Descriptors: Models, Language, Reflection, Writing (Composition)
Khanal, Shristi Shakya; Prasad, P.W.C.; Alsadoon, Abeer; Maag, Angelika – Education and Information Technologies, 2020
The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and…
Descriptors: Electronic Learning, Barriers, Online Courses, Accuracy
Parhizkar, Amirmohammad; Tejeddin, Golnaz; Khatibi, Toktam – Education and Information Technologies, 2023
Increasing productivity in educational systems is of great importance. Researchers are keen to predict the academic performance of students; this is done to enhance the overall productivity of educational system by effectively identifying students whose performance is below average. This universal concern has been combined with data science…
Descriptors: Algorithms, Grade Point Average, Interdisciplinary Approach, Prediction
Iatrellis, Omiros; Savvas, Ilias ?.; Fitsilis, Panos; Gerogiannis, Vassilis C. – Education and Information Technologies, 2021
Learning analytics have proved promising capabilities and opportunities to many aspects of academic research and higher education studies. Data-driven insights can significantly contribute to provide solutions for curbing costs and improving education quality. This paper adopts a two-phase machine learning approach, which utilizes both…
Descriptors: Prediction, Outcomes of Education, Higher Education, Data Analysis
Mimis, Mohamed; El Hajji, Mohamed; Es-saady, Youssef; Oueld Guejdi, Abdellah; Douzi, Hassan; Mammass, Driss – Education and Information Technologies, 2019
The educational recommendation system to provide support for academic guidance and adaptive learning has always been an important issue of research for smart education. A bad guidance can give rise to difficulties in further studies and can be extended to school dropout. This paper explores the potential of Educational Data Mining for academic…
Descriptors: Educational Counseling, Guidance, Educational Research, Data Collection