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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
Caihong Feng; Jingyu Liu; Jianhua Wang; Yunhong Ding; Weidong Ji – Education and Information Technologies, 2025
Student academic performance prediction is a significant area of study in the realm of education that has drawn the interest and investigation of numerous scholars. The current approaches for student academic performance prediction mainly rely on the educational information provided by educational system, ignoring the information on students'…
Descriptors: Academic Achievement, Prediction, Models, Student Behavior
Kajal Mahawar; Punam Rattan – Education and Information Technologies, 2025
Higher education institutions have consistently strived to provide students with top-notch education. To achieve better outcomes, machine learning (ML) algorithms greatly simplify the prediction process. ML can be utilized by academicians to obtain insight into student data and mine data for forecasting the performance. In this paper, the authors…
Descriptors: Electronic Learning, Artificial Intelligence, Academic Achievement, Prediction
Linyan Li; Xiao Bai; Hongshan Xia – Education and Information Technologies, 2024
The higher the level of development of higher education, the larger its contribution to socioeconomic development. In order to predict the trend of higher education development in a country more accurately, a new methodology is employed in this study. A weakening buffer operator-based GM (1, 1) model is constructed using Kazakhstan's gross…
Descriptors: Prediction, Educational Trends, Higher Education, Models
Hayat Sahlaoui; El Arbi Abdellaoui Alaoui; Said Agoujil; Anand Nayyar – Education and Information Technologies, 2024
Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…
Descriptors: Sampling, Classification, Algorithms, Prediction
Xia, Xiaona; Qi, Wanxue – Education and Information Technologies, 2023
MOOCs might be an important organization way to realize the online learning process. Online technology and sharing technology enable MOOCs to realize the adaptive scheduling of learning resources, as well as the independent construction of learning sequences. At the same time, it also generates a large number of complex learning behaviors. How to…
Descriptors: MOOCs, Learning Processes, Learning Analytics, Graphs
Giannakas, Filippos; Troussas, Christos; Krouska, Akrivi; Sgouropoulou, Cleo; Voyiatzis, Ioannis – Education and Information Technologies, 2022
Working in groups is an important collaboration activity in the educational context, where a variety of factors can influence the prediction of the teams' performance. In the pertinent bibliography, several machine learning models are available for delivering predictions. In this sense, the main goal of the current research is to assess 28…
Descriptors: Comparative Analysis, Artificial Intelligence, Prediction, Cooperative Learning
Xiaojing Duan; Bo Pei; G. Alex Ambrose; Arnon Hershkovitz; Ying Cheng; Chaoli Wang – Education and Information Technologies, 2024
Providing educators with understandable, actionable, and trustworthy insights drawn from large-scope heterogeneous learning data is of paramount importance in achieving the full potential of artificial intelligence (AI) in educational settings. Explainable AI (XAI)--contrary to the traditional "black-box" approach--helps fulfilling this…
Descriptors: Academic Achievement, Artificial Intelligence, Prediction, Models
Tao Huang; Jing Geng; Yuxia Chen; Han Wang; Huali Yang; Shengze Hu – Education and Information Technologies, 2024
Digital technology is profoundly transforming various aspects of life, thus highlighting the need to enhance digital literacy on a national scale. In primary and secondary schools, artificial intelligence (AI) education plays a pivotal role in fostering digital literacy. To comprehensively investigate the variables influencing AI education in…
Descriptors: Artificial Intelligence, Elementary Schools, Secondary Schools, Prediction
Meriem Zerkouk; Miloud Mihoubi; Belkacem Chikhaoui; Shengrui Wang – Education and Information Technologies, 2024
School dropout is a significant issue in distance learning, and early detection is crucial for addressing the problem. Our study aims to create a binary classification model that anticipates students' activity levels based on their current achievements and engagement on a Canadian Distance learning Platform. Predicting student dropout, a common…
Descriptors: Artificial Intelligence, Dropouts, Prediction, Distance Education
Badal, Yudish Teshal; Sungkur, Roopesh Kevin – Education and Information Technologies, 2023
The outbreak of COVID-19 has caused significant disruption in all sectors and industries around the world. To tackle the spread of the novel coronavirus, the learning process and the modes of delivery had to be altered. Most courses are delivered traditionally with face-to-face or a blended approach through online learning platforms. In addition,…
Descriptors: Prediction, Models, Learning Analytics, Grades (Scholastic)
Ates, Hüseyin; Garzón, Juan – Education and Information Technologies, 2023
Many studies show that augmented reality (AR) provides multiple benefits to science education, including learning gains, motivation to learn, and collaborative learning. However, while using AR largely depends on the teachers' willingness, existing literature lacks studies that identify teachers' intentions to use this technology. This study…
Descriptors: Models, Intention, Technology Uses in Education, Computer Simulation
Khan, Anupam; Ghosh, Soumya K. – Education and Information Technologies, 2021
Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of educational datasets further catalyse this interestingness, especially in…
Descriptors: Academic Achievement, Prediction, Data Analysis, Meta Analysis
Sghir, Nabila; Adadi, Amina; Lahmer, Mohammed – Education and Information Technologies, 2023
The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, Data Collection
Xiaona Xia; Wanxue Qi – Education and Information Technologies, 2024
The full implementation of MOOCs in online education offers new opportunities for integrating multidisciplinary and comprehensive STEM education. It facilitates the alignment between online learning content and learning behaviors. However, it also presents new challenges, such as a high rate of STEM dropouts. Many learners struggle to establish…
Descriptors: Graphs, MOOCs, STEM Education, Learning Processes