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Nargiza Mikhridinova; Carsten Wolff; Wim Van Petegem – Education and Information Technologies, 2024
An individual competence is one of the main human resources, which enables a person to operate in everyday life. A competence profile, formally captured and described as a structured model, may enable various operations, e.g., a more precise evaluation and closure of a training gap. Such application scenarios supported by information systems are…
Descriptors: Taxonomy, Competence, Models, Profiles
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Rui Yao; Meilin Tian; Chi-Un Lei; Dickson K. W. Chiu – Education and Information Technologies, 2024
Sustainable Development Goals (SDG) 4.7 aims to ensure learners acquire the knowledge and skills for promoting sustainable development by 2030. Yet, Open Educational Resources (OERs) that connect the public with SDGs are currently limitedly assigned and insufficient to promote SDG and sustainability education to support the achievement of SDG 4.7…
Descriptors: Sustainable Development, Open Educational Resources, Sustainability, Classification
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Dongping Wu; Sheiladevi Sukumaran; Xiaomin Zhi; Wenjing Zhou; Lihua Li; Hongnan You – Education and Information Technologies, 2025
With the emerging forces of online and digital products, scholars keenly captured digital literacy and have new research dimensions. The purpose of this study is to present a bibliometric analysis of digital literacy using CiteSpace and to explore the categories, themes and research evolution in digital literacy. A total of 9042 bibliographic…
Descriptors: Classification, Digital Literacy, Bibliometrics, Educational Research
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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
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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
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Anagha Ani; Ean Teng Khor – Education and Information Technologies, 2024
Predictive modelling in the education domain can be utilised to significantly improve teaching and learning experiences. Massive Open Online Courses (MOOCs) generate a large volume of data that can be exploited to predict and evaluate student performance based on various factors. This paper has two broad aims. Firstly, to develop and tune several…
Descriptors: MOOCs, Classification, Artificial Intelligence, Prediction
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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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K. I. Senadhira; R. A. H. M. Rupasingha; B. T. G. S. Kumara – Education and Information Technologies, 2024
The majority of educational institutions around the world have switched to online learning due to the COVID-19 pandemic. Since continuing education has become important during the pandemic as well, academics and students have recognized the value of online learning to avoid their challenges. The objective of this study is to categorize peoples'…
Descriptors: Classification, Artificial Intelligence, Social Media, Electronic Learning
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Gani, Mohammed Osman; Ayyasamy, Ramesh Kumar; Sangodiah, Anbuselvan; Fui, Yong Tien – Education and Information Technologies, 2023
The automated classification of examination questions based on Bloom's Taxonomy (BT) aims to assist the question setters so that high-quality question papers are produced. Most studies to automate this process adopted the machine learning approach, and only a few utilised the deep learning approach. The pre-trained contextual and non-contextual…
Descriptors: Models, Artificial Intelligence, Natural Language Processing, Writing (Composition)
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Quan, Zhi; Pu, Luoxi – Education and Information Technologies, 2023
In the face of surging online education around the globe, it seems quite necessary and helpful for learners and teachers to have the plethora of online resources well sorted out beforehand. To some extent, the efficiency and accuracy of resource search and retrieval may determine the quality and influence of online education. In this research,…
Descriptors: Accuracy, Classification, Internet, Open Educational Resources
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Shoyukhi, Moohebat; Vossen, Paul Hubert; Ahmadi, Abdol Hossein; Kafipour, Reza; Beattie, Kyle Albert – Education and Information Technologies, 2023
Defining "plagiarism" is not simple, and its complexity is too seldom appreciated. This article offers a comprehensive plagiarism assessment rubric from a four-year study of analyzing students' plagiarism. From qualitative analyses of 120 students' paraphrase samples, we identified seven plagiarism dimensions and employed a five-point…
Descriptors: Plagiarism, Writing Evaluation, Scoring Rubrics, Citations (References)
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Umut Zeki; Tolgay Karanfiller; Kamil Yurtkan – Education and Information Technologies, 2024
The developmental, characteristics and educational competencies of students who need special education are developing slowly in compared to their agemates. This is because their expressive language is different. In order to overcome these challenges, assistive technologies can be used under the supervision of the teachers. In this paper, a person…
Descriptors: Special Education, Expressive Language, Assistive Technology, Artificial Intelligence
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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
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Gutiérrez-Santiuste, Elba; Ritacco-Real, Maximiliano – Education and Information Technologies, 2023
This study aims to analyse intercultural communicative competence, understood as the individual's ability to effectively and appropriately develop communication and behaviour, when interacting in an intercultural context. In this study, the Behavioural, Affective and Cognitive Dimensions, and their sub-dimensions, are considered by using…
Descriptors: Intercultural Communication, Communicative Competence (Languages), Classification, Videoconferencing
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Gerardo Ibarra-Vazquez; María Soledad Ramí­rez-Montoya; Hugo Terashima – Education and Information Technologies, 2024
This article aims to study machine learning models to determine their performance in classifying students by gender based on their perception of complex thinking competency. Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity instrument. In this study, we consider the following…
Descriptors: Foreign Countries, College Students, Private Colleges, Gender Bias
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