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

Anibal Flores; Luis Alfaro; Jose Herrera

doi:10.1109/EDUNINE.2019.8875800

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

Anibal Flores, Luis Alfaro, Jose Herrera

Departamento Académico de Ingeniería de Sistemas e Informática

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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).

Original language	English
Title of host publication	EDUNINE 2019 - 3rd IEEE World Engineering Education Conference
Subtitle of host publication	Modern Educational Paradigms for Computer and Engineering Career, Proceedings
Editors	Claudio da Rocha Brito, Melany M. Ciampi
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728116662
DOIs	https://doi.org/10.1109/EDUNINE.2019.8875800
State	Published - Mar 2019
Event	3rd IEEE World Engineering Education Conference, EDUNINE 2019 - Lima, Peru Duration: 17 Mar 2019 → 20 Mar 2019

Publication series

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

Conference

Conference	3rd IEEE World Engineering Education Conference, EDUNINE 2019
Country/Territory	Peru
City	Lima
Period	17/03/19 → 20/03/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

case based reasoning
e-learning
flow theory
personalized learning
q-Learning
reinforcement learning

Access to Document

10.1109/EDUNINE.2019.8875800

Cite this

Flores, A., Alfaro, L., & Herrera, J. (2019). Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning. In C. D. R. Brito, & M. M. Ciampi (Eds.), EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings Article 8875800 (EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EDUNINE.2019.8875800

Flores, Anibal ; Alfaro, Luis ; Herrera, Jose. / Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning. EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings. editor / Claudio da Rocha Brito ; Melany M. Ciampi. Institute of Electrical and Electronics Engineers Inc., 2019. (EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings).

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abstract = "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).",

keywords = "case based reasoning, e-learning, flow theory, personalized learning, q-Learning, reinforcement learning",

author = "Anibal Flores and Luis Alfaro and Jose Herrera",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 3rd IEEE World Engineering Education Conference, EDUNINE 2019 ; Conference date: 17-03-2019 Through 20-03-2019",

year = "2019",

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Flores, A, Alfaro, L & Herrera, J 2019, Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning. in CDR Brito & MM Ciampi (eds), EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings., 8875800, EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings, Institute of Electrical and Electronics Engineers Inc., 3rd IEEE World Engineering Education Conference, EDUNINE 2019, Lima, Peru, 17/03/19. https://doi.org/10.1109/EDUNINE.2019.8875800

Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning. / Flores, Anibal; Alfaro, Luis ; Herrera, Jose.
EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings. ed. / Claudio da Rocha Brito; Melany M. Ciampi. Institute of Electrical and Electronics Engineers Inc., 2019. 8875800 (EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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).

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Flores A, Alfaro L , Herrera J. Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning. In Brito CDR, Ciampi MM, editors, EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8875800. (EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings). doi: 10.1109/EDUNINE.2019.8875800

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

Abstract

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Keywords

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