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Showing 1 to 15 of 20 results Save | Export
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Venera Nakhipova; Yerzhan Kerimbekov; Zhanat Umarova; Halil ibrahim Bulbul; Laura Suleimenova; Elvira Adylbekova – International Journal of Information and Communication Technology Education, 2024
This article introduces a novel method that integrates collaborative filtering into the naive Bayes model to enhance predicting student academic performance. The combined approach leverages collaborative user behavior analysis and probabilistic modeling, showing promising results in improved prediction precision. Collaborative Filtering explores…
Descriptors: Academic Achievement, Prediction, Cooperation, Behavior
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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
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Smithers, Laura – Learning, Media and Technology, 2023
This article examines the work of predictive analytics in shaping the social worlds in which they thrive, and in particular the world of the first year of Great State University's student success initiative. Specifically, this article investigates the following research paradox: predictive analytics, as driven by a logic premised on predicting the…
Descriptors: Prediction, Learning Analytics, Academic Achievement, College Students
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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)
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Shabnam Ara S. J.; Tanuja Ramachandriah; Manjula S. Haladappa – Online Learning, 2025
Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…
Descriptors: Learning Analytics, Privacy, Artificial Intelligence, Regression (Statistics)
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Verger, Mélina; Lallé, Sébastien; Bouchet, François; Luengo, Vanda – International Educational Data Mining Society, 2023
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term…
Descriptors: Prediction, Models, Student Behavior, Academic Achievement
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Selma Tosun; Dilara Bakan Kalaycioglu – Journal of Educational Technology and Online Learning, 2024
Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a…
Descriptors: Academic Achievement, Open Education, Distance Education, Foreign Countries
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Kelli A. Bird; Benjamin L. Castleman; Yifeng Song – Journal of Policy Analysis and Management, 2025
Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models--one predicting course completion, the second predicting degree…
Descriptors: Algorithms, Technology Uses in Education, Bias, Racism
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Hua Ma; Wen Zhao; Yuqi Tang; Peiji Huang; Haibin Zhu; Wensheng Tang; Keqin Li – IEEE Transactions on Learning Technologies, 2024
To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…
Descriptors: College Students, Learning Analytics, Learning Management Systems, Academic Achievement
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Tanjea Ane; Tabatshum Nepa – Research on Education and Media, 2024
Precision education derives teaching and learning opportunities by customizing predictive rules in educational methods. Innovative educational research faces new challenges and affords state-of-the-art methods to trace knowledge between the teaching and learning ecosystem. Individual intelligence can only be captured through knowledge level…
Descriptors: Artificial Intelligence, Prediction, Models, Teaching Methods
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Xu, Tonghui – Journal of Educators Online, 2023
The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data…
Descriptors: College Students, Online Courses, Academic Achievement, Data Analysis
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Ean Teng Khor; Dave Darshan – International Journal of Information and Learning Technology, 2024
Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the…
Descriptors: Prediction, Academic Achievement, Electronic Learning, Artificial Intelligence
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Mohamed, Mohamed Hegazy; Abdelgaber, Sayed; Abd-Ellatif, Laila – Journal of Education and e-Learning Research, 2023
Governments and educational authorities around the world are emphasizing performance evaluation of educational systems. Opinion Mining (OM) has gained acceptance among experts in various regions, including the preparation space. The proposed model involves Two modules: the data preprocessing module and the opinion mining module. The main objective…
Descriptors: Educational Practices, Program Evaluation, Opinions, Data Collection
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Kukkar, Ashima; Mohana, Rajni; Sharma, Aman; Nayyar, Anand – Education and Information Technologies, 2023
Predicting student performance is crucial in higher education, as it facilitates course selection and the development of appropriate future study plans. The process of supporting the instructors and supervisors in monitoring students in order to upkeep them and combine training programs to get the best outcomes. It decreases the official warning…
Descriptors: Academic Achievement, Mental Health, Well Being, Interaction
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Hao Wu; Shan Li; Ying Gao; Jinta Weng; Guozhu Ding – Education and Information Technologies, 2024
Natural language processing (NLP) has captivated the attention of educational researchers over the past three decades. In this study, a total of 2,480 studies were retrieved through a comprehensive literature search. We used neural topic modeling and pre-trained language modeling to explore the research topics pertaining to the application of NLP…
Descriptors: Natural Language Processing, Educational Research, Research Design, Educational Trends
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