Publication Date
In 2025 | 2 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 3 |
Descriptor
Evaluation Methods | 3 |
Natural Language Processing | 3 |
Artificial Intelligence | 2 |
Computer Software | 2 |
Models | 2 |
Accuracy | 1 |
Algorithms | 1 |
Architecture | 1 |
Automation | 1 |
Climate | 1 |
Compliance (Legal) | 1 |
More ▼ |
Source
Education and Information… | 3 |
Author
Chengyang Qian | 1 |
Dasgupta, Ranjan | 1 |
Harun Çelik | 1 |
Hüseyin Miraç Pektas | 1 |
Kangkang Li | 1 |
Sengupta, Souvik | 1 |
Xianmin Yang | 1 |
Yunus Kökver | 1 |
Publication Type
Journal Articles | 3 |
Reports - Research | 2 |
Reports - Descriptive | 1 |
Education Level
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Kangkang Li; Chengyang Qian; Xianmin Yang – Education and Information Technologies, 2025
In learnersourcing, automatic evaluation of student-generated content (SGC) is significant as it streamlines the evaluation process, provides timely feedback, and enhances the objectivity of grading, ultimately supporting more effective and efficient learning outcomes. However, the methods of aggregating students' evaluations of SGC face the…
Descriptors: Student Developed Materials, Educational Quality, Automation, Artificial Intelligence
Yunus Kökver; Hüseyin Miraç Pektas; Harun Çelik – Education and Information Technologies, 2025
This study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design…
Descriptors: Artificial Intelligence, Misconceptions, Preservice Teachers, Natural Language Processing
Sengupta, Souvik; Dasgupta, Ranjan – Education and Information Technologies, 2017
This paper illustrates an approach for architectural design of a Learning Management System (LMS), which is verifiable against the Learning Technology System Architecture (LTSA) conformance rules. We introduce a new method for software architectural design that extends the Unified Modeling Language (UML) component diagram with the formal…
Descriptors: Architecture, Integrated Learning Systems, Educational Technology, Computer Software