Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning

Anibal Flores, Luis Alfaro, Jose Herrera

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

This paper presents a proposal model for implementing personalized e-learning. The proposal model considers the level of skills or knowledge that a student has on a particular subject; this is determined through a pretest; this aspect is very important to avoid problems as anxiety or boredom according flow theory. In addition, in an e-learning system to determine the optimal sequence of learning resources for a student, we will work in a complementary manner with two machine-learning techniques: Case Based Reasoning and Reinforcement Learning (Q-Learning). The Case Based Reasoning, will allow based on previous success cases, determine the sequence of learning resources most appropriate for the student; and if there are not very similar cases, a learning sequence will be chosen from the proposed ones by Reinforcement Learning (Q-Learning).

Idioma originalInglés
Título de la publicación alojadaEDUNINE 2019 - 3rd IEEE World Engineering Education Conference
Subtítulo de la publicación alojadaModern Educational Paradigms for Computer and Engineering Career, Proceedings
EditoresClaudio da Rocha Brito, Melany M. Ciampi
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728116662
DOI
EstadoPublicada - mar. 2019
Evento3rd IEEE World Engineering Education Conference, EDUNINE 2019 - Lima, Perú
Duración: 17 mar. 201920 mar. 2019

Serie de la publicación

NombreEDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings

Conferencia

Conferencia3rd IEEE World Engineering Education Conference, EDUNINE 2019
País/TerritorioPerú
CiudadLima
Período17/03/1920/03/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

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