Publication Date
| In 2026 | 0 |
| Since 2025 | 2 |
| Since 2022 (last 5 years) | 3 |
| Since 2017 (last 10 years) | 3 |
| Since 2007 (last 20 years) | 3 |
Descriptor
| Electronic Learning | 3 |
| Natural Language Processing | 3 |
| Artificial Intelligence | 2 |
| Academic Failure | 1 |
| At Risk Students | 1 |
| Automation | 1 |
| Causal Models | 1 |
| Courseware | 1 |
| Dropouts | 1 |
| Foreign Countries | 1 |
| Generalizability Theory | 1 |
| More ▼ | |
Source
| Journal of Computer Assisted… | 3 |
Author
| Angelina Gašpar | 1 |
| Ani Grubišic | 1 |
| Anique de Bruin | 1 |
| Branko Žitko | 1 |
| Hui Shi | 1 |
| Hunhui Na | 1 |
| Héctor J. Pijeira-Díaz | 1 |
| Ines Šaric-Grgic | 1 |
| Janneke van de Pol | 1 |
| Nuodi Zhang | 1 |
| Secil Caskurlu | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 3 |
| Reports - Research | 3 |
| Information Analyses | 1 |
Education Level
Audience
Location
| Netherlands | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Ani Grubišic; Ines Šaric-Grgic; Angelina Gašpar; Branko Žitko – Journal of Computer Assisted Learning, 2025
Background: Adaptive educational systems have gained increasing attention due to their ability to personalise educational content based on individual learner progress. Prior research highlights that intelligent tutoring systems (ITSs) and adaptive courseware models improve learning outcomes by dynamically adjusting instructional materials.…
Descriptors: Usability, Courseware, Natural Language Processing, Intelligent Tutoring Systems
Hui Shi; Nuodi Zhang; Secil Caskurlu; Hunhui Na – Journal of Computer Assisted Learning, 2025
Background: The growth of online education has provided flexibility and access to a wide range of courses. However, the self-paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at-risk students, but multiple issues have not been…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, At Risk Students
Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models

Peer reviewed
Direct link
