Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 0 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Comparative Analysis | 2 |
| Error Patterns | 2 |
| Intelligent Tutoring Systems | 2 |
| Models | 2 |
| Prediction | 2 |
| Classification | 1 |
| Computer Software | 1 |
| Data Analysis | 1 |
| Dyslexia | 1 |
| Educational Experiments | 1 |
| Educational Games | 1 |
| More ▼ | |
Author
| Gross, Markus | 2 |
| Baschera, Gian-Marco | 1 |
| Klingler, Severin | 1 |
| Käser, Tanja | 1 |
| Solenthaler, Barbara | 1 |
Publication Type
| Reports - Research | 2 |
| Journal Articles | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| Elementary Education | 1 |
| Elementary Secondary Education | 1 |
Audience
Location
| Switzerland | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Download full textKlingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2015
Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge…
Descriptors: Intelligent Tutoring Systems, Knowledge Level, Item Response Theory, Prediction
Peer reviewed
Direct linkBaschera, Gian-Marco; Gross, Markus – International Journal of Artificial Intelligence in Education, 2010
We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…
Descriptors: Foreign Countries, Spelling, Intelligent Tutoring Systems, Prediction
