Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 3 |
Descriptor
| Classification | 4 |
| Data Processing | 4 |
| Models | 4 |
| Data Analysis | 3 |
| Artificial Intelligence | 2 |
| Information Needs | 2 |
| Prediction | 2 |
| Cluster Analysis | 1 |
| College Administration | 1 |
| College Instruction | 1 |
| College Students | 1 |
| More ▼ | |
Author
Publication Type
| Speeches/Meeting Papers | 4 |
| Reports - Research | 2 |
| Reports - Evaluative | 1 |
Education Level
| Higher Education | 1 |
Audience
Location
| Florida | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Seyed Parsa Neshaei; Richard Lee Davis; Paola Mejia-Domenzain; Tanya Nazaretsky; Tanja Käser – International Educational Data Mining Society, 2025
Deep learning models for text classification have been increasingly used in intelligent tutoring systems and educational writing assistants. However, the scarcity of data in many educational settings, as well as certain imbalances in counts among the annotated labels of educational datasets, limits the generalizability and expressiveness of…
Descriptors: Artificial Intelligence, Classification, Natural Language Processing, Technology Uses in Education
Klingler, Severin; Wampfler, Rafael; Käser, Tanja; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2017
Gathering labeled data in educational data mining (EDM) is a time and cost intensive task. However, the amount of available training data directly influences the quality of predictive models. Unlabeled data, on the other hand, is readily available in high volumes from intelligent tutoring systems and massive open online courses. In this paper, we…
Descriptors: Classification, Artificial Intelligence, Networks, Learning Disabilities
Michalski, Greg V. – Association for Institutional Research (NJ1), 2011
Excessive college course withdrawals are costly to the student and the institution in terms of time to degree completion, available classroom space, and other resources. Although generally well quantified, detailed analysis of the reasons given by students for course withdrawal is less common. To address this, a text mining analysis was performed…
Descriptors: College Instruction, Courses, Withdrawal (Education), College Students
Baltes, Kenneth G.; Hendrix, Vernon L. – 1978
Two recent developments in management information system technology and higher education administration have brought about the need for this study, designed to develop a methodology for revealing a relational model of the data base that administrators are operating from currently or would like to be able to operate from in the future.…
Descriptors: Classification, Cluster Analysis, College Administration, Data Analysis

Peer reviewed
