Publication Date
| In 2026 | 0 |
| Since 2025 | 38 |
| Since 2022 (last 5 years) | 189 |
Descriptor
| Artificial Intelligence | 189 |
| Classification | 188 |
| Models | 52 |
| Prediction | 46 |
| Accuracy | 40 |
| Algorithms | 37 |
| Foreign Countries | 35 |
| Natural Language Processing | 33 |
| Technology Uses in Education | 30 |
| Computer Software | 28 |
| Automation | 24 |
| More ▼ | |
Source
Author
| Di Zou | 3 |
| Haoran Xie | 3 |
| Carolyn M. Forsyth | 2 |
| Chao, Jie | 2 |
| Chen, Guanliang | 2 |
| Conrad Borchers | 2 |
| Donald, Claire | 2 |
| Fu Lee Wang | 2 |
| Gaševic, Dragan | 2 |
| Giacaman, Nasser | 2 |
| Holmes, Wayne | 2 |
| More ▼ | |
Publication Type
Education Level
Audience
| Practitioners | 1 |
| Students | 1 |
| Teachers | 1 |
Location
| Turkey | 7 |
| South Korea | 5 |
| Africa | 2 |
| Australia | 2 |
| Florida | 2 |
| Germany | 2 |
| Ghana | 2 |
| India | 2 |
| Mexico | 2 |
| Michigan | 2 |
| United Kingdom | 2 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkTianjiao Zhao; Jiayi Jia; Tianfei Zhu; Junyu Yang – International Journal of Technology and Design Education, 2024
Designers are always pursuing design with suitable emotions. Effective emotional fusion not only produces a good user experience but also extends the product lifecycle. The decoding of design emotion and the use of design emotion language should run through the entire design process. In this study, we propose a new emotion-embedded design flow…
Descriptors: Psychological Patterns, Design, Artificial Intelligence, Databases
Download full textWan-Chong Choi; Chan-Tong Lam; António José Mendes – International Educational Data Mining Society, 2025
Missing data presents a significant challenge in Educational Data Mining (EDM). Imputation techniques aim to reconstruct missing data while preserving critical information in datasets for more accurate analysis. Although imputation techniques have gained attention in various fields in recent years, their use for addressing missing data in…
Descriptors: Research Problems, Data Analysis, Research Methodology, Models
A Comparison of Real-Time User Classification Methods Using Interaction Data for Open-Ended Learning
Rohit Murali; Cristina Conati; David Poole – International Educational Data Mining Society, 2025
When tutoring students it is useful to be able to predict whether they are succeeding as early as possible. This paper compares multiple methods for predicting from sequential interaction data whether a student is on a successful path. Predicting students' future performance and intervening has shown promise in improving learner outcomes and…
Descriptors: Classification, Prediction, Markov Processes, Artificial Intelligence
Ajay Verma; Manisha Jain – Measurement: Interdisciplinary Research and Perspectives, 2025
Purpose: This research employs machine learning and mediation analysis, along with path analysis, to investigate the correlations between factors such as body mass index (BMI) and the occurrence of diabetes and heart disease among the Indian population. The objective is to enhance models that are specifically designed to accommodate lifestyles,…
Descriptors: Diabetes, Heart Disorders, Risk, Prediction
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
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
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
Di Zou; Haoran Xie; Lucas Kohnke – European Journal of Education, 2025
As artificial intelligence (AI) rapidly transforms educational practices, educators worldwide face an urgent need to develop pedagogic competencies that align with AI's evolving capabilities, yet existing frameworks lack systematic guidance for AI-specific skill development. This article introduces a pioneering framework designed to refine…
Descriptors: Teacher Competencies, Artificial Intelligence, Pedagogical Content Knowledge, Technological Literacy
Sophia Mavridi – Technology in Language Teaching & Learning, 2025
This article proposes a critical typology of five emerging responses to artificial intelligence (AI) in language education, from prohibition and hype to critical engagement, highlighting the assumptions, tensions, and possibilities each orientation embodies. This typology serves as a reflective tool to examine how educators and institutions are…
Descriptors: Artificial Intelligence, Classification, Responses, Language Teachers
R. Thapa; A. Garikipati; M. Ciobanu; N.P. Singh; E. Browning; J. DeCurzio; G. Barnes; F.A. Dinenno; Q. Mao; R. Das – Journal of Autism and Developmental Disorders, 2024
Purpose: Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum…
Descriptors: Autism Spectrum Disorders, Symptoms (Individual Disorders), Clinical Diagnosis, Artificial Intelligence
Meng Cao; Philip I. Pavlik Jr.; Wei Chu; Liang Zhang – International Educational Data Mining Society, 2024
In category learning, a growing body of literature has increasingly focused on exploring the impacts of interleaving in contrast to blocking. The sequential attention hypothesis posits that interleaving draws attention to the differences between categories while blocking directs attention toward similarities within categories [4, 5]. Although a…
Descriptors: Attention, Algorithms, Artificial Intelligence, Classification
Kataoka, Yuki; Taito, Shunsuke; Yamamoto, Norio; So, Ryuhei; Tsutsumi, Yusuke; Anan, Keisuke; Banno, Masahiro; Tsujimoto, Yasushi; Wada, Yoshitaka; Sagami, Shintaro; Tsujimoto, Hiraku; Nihashi, Takashi; Takeuchi, Motoki; Terasawa, Teruhiko; Iguchi, Masahiro; Kumasawa, Junji; Ichikawa, Takumi; Furukawa, Ryuki; Yamabe, Jun; Furukawa, Toshi A. – Research Synthesis Methods, 2023
There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts…
Descriptors: Competition, Classification, Diagnostic Tests, Accuracy
Hu, Yuanyuan; Donald, Claire; Giacaman, Nasser – International Journal of Artificial Intelligence in Education, 2023
This paper investigates using multi-label deep learning approach to extending the understanding of cognitive presence in MOOC discussions. Previous studies demonstrate the challenges of subjectivity in manual categorisation methods. Training automatic single-label classifiers may preserve this subjectivity. Using a triangulation approach, we…
Descriptors: Classification, MOOCs, Artificial Intelligence, Intelligent Tutoring Systems
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
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
