Conference
Optimizing human learning using reinforcement learning ; Optimiser l'apprentissage humain avec de l'apprentissage par renforcement
| العنوان: | Optimizing human learning using reinforcement learning ; Optimiser l'apprentissage humain avec de l'apprentissage par renforcement |
|---|---|
| المؤلفون: | Girard, Samuel, Vie, Jill-Jênn, Tort, Françoise, Bouzeghoub, Amel |
| المساهمون: | Méthodes computationnelles et mathématiques pour comprendre la société et la santé à partir de données (SODA), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), PIX, Institut Polytechnique de Paris (IP Paris), Département Informatique (TSP - INF), Institut Mines-Télécom Paris (IMT)-Télécom SudParis (TSP), Architecture, Cloud continuum, formal Models, artificial intElligence and Services in distributed computing (ACMES-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom Paris (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom Paris (IMT)-Télécom SudParis (TSP) |
| المصدر: | Educational Data Mining 2024 ; https://hal.science/hal-04637464 ; Educational Data Mining 2024, Jul 2024, Atlanta (USA), United States |
| بيانات النشر: | HAL CCSD |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Reinforcement Learning, Intelligent Tutoring Systems, Partially Observable Markov Decision Processes, Knowledge Tracing, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] |
| جغرافية الموضوع: | Atlanta (USA), United States |
| الوصف: | International audience ; Education is a field greatly impacted by the digital revolution. Online courses and MOOCs give access to education to most parts of the world, and many assessments are made online as they are %it is easier to evaluate. This creates an important collection of learning analytics that can be used to provide and generate personalized content, which is essential to keep learners engaged and increase learning gains. This thesis aims to see how machine learning algorithms can be used to learn better knowledge representations of learners and consequently to recommend learning tasks (exercises or courses) tailored to a student's needs. We are learning instructional policies from student data so that we can understand how students learn and which lessons/exercises in a course strongly impact learning for which students. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| Relation: | hal-04637464; https://hal.science/hal-04637464; https://hal.science/hal-04637464/document; https://hal.science/hal-04637464/file/Doctoral_consortium-5.pdf |
| الاتاحة: | https://hal.science/hal-04637464 https://hal.science/hal-04637464/document https://hal.science/hal-04637464/file/Doctoral_consortium-5.pdf |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.F8FEF1F8 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |