Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 5 |
| Since 2017 (last 10 years) | 5 |
| Since 2007 (last 20 years) | 5 |
Descriptor
| Algorithms | 6 |
| Information Retrieval | 6 |
| Pattern Recognition | 6 |
| Data Analysis | 5 |
| Artificial Intelligence | 3 |
| Academic Achievement | 2 |
| Prediction | 2 |
| Cataloging | 1 |
| Cluster Grouping | 1 |
| College Students | 1 |
| Computer System Design | 1 |
| More ▼ | |
Source
| International Educational… | 2 |
| Education and Information… | 1 |
| Information Processing and… | 1 |
| International Journal of… | 1 |
| Journal of Educators Online | 1 |
Author
| Chenguang Pan | 1 |
| Guiyun Feng | 1 |
| Honghui Chen | 1 |
| Karimov, Ayaz | 1 |
| Khor, Ean Teng | 1 |
| Kärkkäinen, Tommi | 1 |
| Molto, Mavis | 1 |
| Saarela, Mirka | 1 |
| Svenonius, Elaine | 1 |
| Xu, Tonghui | 1 |
| Zhou Zhang | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 6 |
| Journal Articles | 4 |
| Speeches/Meeting Papers | 2 |
| Information Analyses | 1 |
| Reports - Descriptive | 1 |
Education Level
| Secondary Education | 2 |
| Elementary Education | 1 |
| Grade 5 | 1 |
| Grade 6 | 1 |
| Grade 7 | 1 |
| High Schools | 1 |
| Higher Education | 1 |
| Intermediate Grades | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Postsecondary Education | 1 |
| More ▼ | |
Audience
| Policymakers | 1 |
| Researchers | 1 |
Location
| Azerbaijan | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Guiyun Feng; Honghui Chen – Education and Information Technologies, 2025
Data mining has been successfully and widely utilized in educational information systems, and an important research field has been formed, which is educational data mining. Process mining inherits the characteristics of data mining which can not only use historical data in the system to analyze learning behavior and predict academic performance,…
Descriptors: Educational Research, Artificial Intelligence, Data Use, Algorithms
Khor, Ean Teng – International Journal of Information and Learning Technology, 2022
Purpose: The purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance. Design/methodology/approach: For the first step, the author performed exploratory data analysis to analyze the dataset. The process was then…
Descriptors: Prediction, Low Achievement, Algorithms, Artificial Intelligence
Chenguang Pan; Zhou Zhang – International Educational Data Mining Society, 2024
There is less attention on examining algorithmic fairness in secondary education dropout predictions. Also, the inclusion of protected attributes in machine learning models remains a subject of debate. This study delves into the use of machine learning models for predicting high school dropouts, focusing on the role of protected attributes like…
Descriptors: High School Students, Dropouts, Dropout Characteristics, Artificial Intelligence
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
Karimov, Ayaz; Saarela, Mirka; Kärkkäinen, Tommi – International Educational Data Mining Society, 2023
Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played…
Descriptors: Student Attitudes, Feedback (Response), Educational Games, Information Retrieval
Peer reviewedMolto, Mavis; Svenonius, Elaine – Information Processing and Management, 1991
Study results indicate that it is feasible to develop automatic name recognition algorithms to distinguish character strings representing names from other character strings occurring in English language titles. This finding offers cautious promise for alleviating some of the labor intensive work of cataloging. (16 references) (Author/SD)
Descriptors: Algorithms, Cataloging, Computer System Design, Expert Systems

Direct link
